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ML Papers Explained

Explanations to key concepts in ML

Language Models

Paper	Date	Description
Transformer	June 2017	An Encoder Decoder model, that introduced multihead attention mechanism for language translation task.
Elmo	February 2018	Deep contextualized word representations that captures both intricate aspects of word usage and contextual variations across language contexts.
Marian MT	April 2018	A Neural Machine Translation framework written entirely in C++ with minimal dependencies, designed for high training and translation speed.
GPT	June 2018	A Decoder only transformer which is autoregressively pretrained and then finetuned for specific downstream tasks using task-aware input transformations.
BERT	October 2018	Introduced pre-training for Encoder Transformers. Uses unified architecture across different tasks.
Transformer XL	January 2019	Extends the original Transformer model to handle longer sequences of text by introducing recurrence into the self-attention mechanism.
XLM	January 2019	Proposes two methods to learn cross-lingual language models (XLMs): one unsupervised that only relies on monolingual data, and one supervised that leverages parallel data with a new cross-lingual language model objective.
GPT 2	February 2019	Demonstrates that language models begin to learn various language processing tasks without any explicit supervision.
Sparse Transformer	April 2019	Introduced sparse factorizations of the attention matrix to reduce the time and memory consumption to O(n√ n) in terms of sequence lengths.
UniLM	May 2019	Utilizes a shared Transformer network and specific self-attention masks to excel in both language understanding and generation tasks.
XLNet	June 2019	Extension of the Transformer-XL, pre-trained using a new method that combines ideas from AR and AE objectives.
RoBERTa	July 2019	Built upon BERT, by carefully optimizing hyperparameters and training data size to improve performance on various language tasks .
Sentence BERT	August 2019	A modification of BERT that uses siamese and triplet network structures to derive sentence embeddings that can be compared using cosine-similarity.
CTRL	September 2019	A 1.63B language model that can generate text conditioned on control codes that govern style, content, and task-specific behavior, allowing for more explicit control over text generation.
Tiny BERT	September 2019	Uses attention transfer, and task specific distillation for distilling BERT.
ALBERT	September 2019	Presents certain parameter reduction techniques to lower memory consumption and increase the training speed of BERT.
Distil BERT	October 2019	Distills BERT on very large batches leveraging gradient accumulation, using dynamic masking and without the next sentence prediction objective.
Distil RoBERTa	October 2019	Distillation of RoBERTa, using the same techniques as Distil BERT.
T5	October 2019	A unified encoder-decoder framework that converts all text-based language problems into a text-to-text format.
BART	October 2019	An Encoder-Decoder pretrained to reconstruct the original text from corrupted versions of it.
XLM-Roberta	November 2019	A multilingual masked language model pre-trained on text in 100 languages, shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of crosslingual transfer tasks.
XLM-Roberta	November 2019	A multilingual masked language model pre-trained on text in 100 languages, shows that pretraining multilingual language models at scale leads to significant performance gains for a wide range of crosslingual transfer tasks.
Pegasus	December 2019	A self-supervised pre-training objective for abstractive text summarization, proposes removing/masking important sentences from an input document and generating them together as one output sequence.
Reformer	January 2020	Improves the efficiency of Transformers by replacing dot-product attention with locality-sensitive hashing (O(Llog L) complexity), using reversible residual layers to store activations only once, and splitting feed-forward layer activations into chunks, allowing it to perform on par with Transformer models while being much more memory-efficient and faster on long sequences.
mBART	January 2020	A multilingual sequence-to-sequence denoising auto-encoder that pre-trains a complete autoregressive model on large-scale monolingual corpora across many languages using the BART objective, achieving significant performance gains in machine translation tasks.
UniLMv2	February 2020	Utilizes a pseudo-masked language model (PMLM) for both autoencoding and partially autoregressive language modeling tasks,significantly advancing the capabilities of language models in diverse NLP tasks.
ELECTRA	March 2020	Proposes a sample-efficient pre-training task called replaced token detection, which corrupts input by replacing some tokens with plausible alternatives and trains a discriminative model to predict whether each token was replaced or no.
FastBERT	April 2020	A speed-tunable encoder with adaptive inference time having branches at each transformer output to enable early outputs.
MobileBERT	April 2020	Compressed and faster version of the BERT, featuring bottleneck structures, optimized attention mechanisms, and knowledge transfer.
Longformer	April 2020	Introduces a linearly scalable attention mechanism, allowing handling texts of exteded length.
GPT 3	May 2020	Demonstrates that scaling up language models greatly improves task-agnostic, few-shot performance.
DeBERTa	June 2020	Enhances BERT and RoBERTa through disentangled attention mechanisms, an enhanced mask decoder, and virtual adversarial training.
DeBERTa v2	June 2020	Enhanced version of the DeBERTa featuring a new vocabulary, nGiE integration, optimized attention mechanisms, additional model sizes, and improved tokenization.
T5 v1.1	July 2020	An enhanced version of the original T5 model, featuring improvements such as GEGLU activation, no dropout in pre-training, exclusive pre-training on C4, no parameter sharing between embedding and classifier layers.
mT5	October 2020	A multilingual variant of T5 based on T5 v1.1, pre-trained on a new Common Crawl-based dataset covering 101 languages (mC4).
Codex	July 2021	A GPT language model finetuned on publicly available code from GitHub.
FLAN	September 2021	An instruction-tuned language model developed through finetuning on various NLP datasets described by natural language instructions.
T0	October 2021	A fine tuned encoder-decoder model on a multitask mixture covering a wide variety of tasks, attaining strong zero-shot performance on several standard datasets.
DeBERTa V3	November 2021	Enhances the DeBERTa architecture by introducing replaced token detection (RTD) instead of mask language modeling (MLM), along with a novel gradient-disentangled embedding sharing method, exhibiting superior performance across various natural language understanding tasks.
WebGPT	December 2021	A fine-tuned GPT-3 model utilizing text-based web browsing, trained via imitation learning and human feedback, enhancing its ability to answer long-form questions with factual accuracy.
Gopher	December 2021	Provides a comprehensive analysis of the performance of various Transformer models across different scales upto 280B on 152 tasks.
LaMDA	January 2022	Transformer based models specialized for dialog, which are pre-trained on public dialog data and web text.
BERTopic	March 20222	Utilizes Sentence-BERT for document embeddings, UMAP, HDBSCAN (soft-clustering), and an adjusted class-based TF-IDF, addressing multiple topics per document and dynamic topics' linear evolution.
Instruct GPT	March 2022	Fine-tuned GPT using supervised learning (instruction tuning) and reinforcement learning from human feedback to align with user intent.
CodeGen	March 2022	An LLM trained for program synthesis using input-output examples and natural language descriptions.
Chinchilla	March 2022	Investigated the optimal model size and number of tokens for training a transformer LLM within a given compute budget (Scaling Laws).
PaLM	April 2022	A 540-B parameter, densely activated, Transformer, trained using Pathways, (ML system that enables highly efficient training across multiple TPU Pods).
GPT-NeoX-20B	April 2022	An autoregressive LLM trained on the Pile, and the largest dense model that had publicly available weights at the time of submission.
OPT	May 2022	A suite of decoder-only pre-trained transformers with parameter ranges from 125M to 175B. OPT-175B being comparable to GPT-3.
Flan T5, Flan PaLM	October 2022	Explores instruction fine tuning with a particular focus on scaling the number of tasks, scaling the model size, and fine tuning on chain-of-thought data.
BLOOM	November 2022	A 176B-parameter open-access decoder-only transformer, collaboratively developed by hundreds of researchers, aiming to democratize LLM technology.
BLOOMZ, mT0	November 2022	Applies Multitask prompted fine tuning to the pretrained multilingual models on English tasks with English prompts to attain task generalization to non-English languages that appear only in the pretraining corpus.
Galactica	November 2022	An LLM trained on scientific data thus specializing in scientific knowledge.
ChatGPT	November 2022	An interactive model designed to engage in conversations, built on top of GPT 3.5.
Self Instruct	December 2022	A framework for improving the instruction-following capabilities of pretrained language models by bootstrapping off their own generations.
LLaMA	February 2023	A collection of foundation LLMs by Meta ranging from 7B to 65B parameters, trained using publicly available datasets exclusively.
Toolformer	February 2023	An LLM trained to decide which APIs to call, when to call them, what arguments to pass, and how to best incorporate the results into future token prediction.
Alpaca	March 2023	A fine-tuned LLaMA 7B model, trained on instruction-following demonstrations generated in the style of self-instruct using text-davinci-003.
GPT 4	March 2023	A multimodal transformer model pre-trained to predict the next token in a document, which can accept image and text inputs and produce text outputs.
Vicuna	March 2023	A 13B LLaMA chatbot fine tuned on user-shared conversations collected from ShareGPT, capable of generating more detailed and well-structured answers compared to Alpaca.
BloombergGPT	March 2023	A 50B language model train on general purpose and domain specific data to support a wide range of tasks within the financial industry.
Pythia	April 2023	A suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters.
WizardLM	April 2023	Introduces Evol-Instruct, a method to generate large amounts of instruction data with varying levels of complexity using LLM instead of humans to fine tune a Llama model
CodeGen2	May 2023	Proposes an approach to make the training of LLMs for program synthesis more efficient by unifying key components of model architectures, learning methods, infill sampling, and data distributions
PaLM 2	May 2023	Successor of PALM, trained on a mixture of different pre-training objectives in order to understand different aspects of language.
LIMA	May 2023	A LLaMa model fine-tuned on only 1,000 carefully curated prompts and responses, without any reinforcement learning or human preference modeling.
Gorilla	May 2023	A retrieve-aware finetuned LLaMA-7B model, specifically for API calls.
Orca	June 2023	Presents a novel approach that addresses the limitations of instruction tuning by leveraging richer imitation signals, scaling tasks and instructions, and utilizing a teacher assistant to help with progressive learning.
Falcon	June 2023	An Open Source LLM trained on properly filtered and deduplicated web data alone.
Phi-1	June 2023	An LLM for code, trained using a textbook quality data from the web and synthetically generated textbooks and exercises with GPT-3.5.
WizardCoder	June 2023	Enhances the performance of the open-source Code LLM, StarCoder, through the application of Code Evol-Instruct.
LLaMA 2	July 2023	Successor of LLaMA. LLaMA 2-Chat is optimized for dialogue use cases.
Tool LLM	July 2023	A LLaMA model finetuned on an instruction-tuning dataset for tool use, automatically created using ChatGPT.
Humpback	August 2023	LLaMA finetuned using Instrustion backtranslation.
Code LLaMA	August 2023	LLaMA 2 based LLM for code.
WizardMath	August 2023	Proposes Reinforcement Learning from Evol-Instruct Feedback (RLEIF) method, applied to Llama-2 to enhance the mathematical reasoning abilities.
LLaMA 2 Long	September 2023	A series of long context LLMs s that support effective context windows of up to 32,768 tokens.
Phi-1.5	September 2023	Follows the phi-1 approach, focusing this time on common sense reasoning in natural language.
MAmmoTH	September 2023	A series of LLMs specifically designed for general math problem-solving, trained on MathInstruct, a dataset compiled from 13 math datasets with intermediate rationales that combines chain-of-thought and program-of-thought approaches to accommodate different thought processes for various math problems.
Mistral 7B	October 2023	Leverages grouped-query attention for faster inference, coupled with sliding window attention to effectively handle sequences of arbitrary length with a reduced inference cost.
Llemma	October 2023	An LLM for mathematics, formed by continued pretraining of Code Llama on a mixture of scientific papers, web data containing mathematics, and mathematical code.
CodeFusion	October 2023	A diffusion code generation model that iteratively refines entire programs based on encoded natural language, overcoming the limitation of auto-regressive models in code generation by allowing reconsideration of earlier tokens.
Zephyr 7B	October 2023	Utilizes dDPO and AI Feedback (AIF) preference data to achieve superior intent alignment in chat-based language modeling.
Grok 1	November 2023	A 314B Mixture-of-Experts model, modeled after the Hitchhiker's Guide to the Galaxy, designed to be witty.
Orca 2	November 2023	Introduces Cautious Reasoning for training smaller models to select the most effective solution strategy based on the problem at hand, by crafting data with task-specific system instruction(s) corresponding to the chosen strategy in order to obtain teacher responses for each task and replacing the student’s system instruction with a generic one vacated of details of how to approach the task.
Phi-2	December 2023	A 2.7B model, developed to explore whether emergent abilities achieved by large-scale language models can also be achieved at a smaller scale using strategic choices for training, such as data selection.
TinyLlama	January 2024	A 1.1B language model built upon the architecture and tokenizer of Llama 2, pre-trained on around 1 trillion tokens for approximately 3 epochs, leveraging FlashAttention and Grouped Query Attention, to achieve better computational efficiency.
Mixtral 8x7B	January 2024	A Sparse Mixture of Experts language model trained with multilingual data using a context size of 32k tokens.
H2O Danube 1.8B	January 2024	A language model trained on 1T tokens following the core principles of LLama 2 and Mistral, leveraging and refining various techniques for pre-training large language models.
OLMo	February 2024	A state-of-the-art, truly open language model and framework that includes training data, code, and tools for building, studying, and advancing language models.
MobileLLM	February 2024	Leverages various architectures and attention mechanisms to achieve a strong baseline network, which is then improved upon by introducing an immediate block-wise weight-sharing approach, resulting in a further accuracy boost.
Orca Math	February 2024	A fine tuned Mistral-7B that excels at math problems without external tools, utilizing a high-quality synthetic dataset of 200K problems created through multi-agent collaboration and an iterative learning process that involves practicing problem-solving, receiving feedback, and learning from preference pairs incorporating the model's solutions and feedback.
Gemma	February 2024	A family of 2B and 7B, state-of-the-art language models based on Google's Gemini models, offering advancements in language understanding, reasoning, and safety.
Aya 101	Februray 2024	A massively multilingual generative language model that follows instructions in 101 languages,trained by finetuning mT5.
Nemotron-4 15B	February 2024	A 15B multilingual language model trained on 8T text tokens by Nvidia.
Hawk, Griffin	February 2024	Introduces Real Gated Linear Recurrent Unit Layer that forms the core of the new recurrent block, replacing Multi-Query Attention for better efficiency and scalability
WRAP	March 2024	Uses an off-the-shelf instruction-tuned model prompted to paraphrase documents on the web in specific styles to jointly pre-train LLMs on real and synthetic rephrases.
Command R	March 2024	An LLM optimized for retrieval-augmented generation and tool use, across multiple languages.
DBRX	March 2024	A 132B open, general-purpose fine grained Sparse MoE LLM surpassing GPT-3.5 and competitive with Gemini 1.0 Pro.
Grok 1.5	March 2024	An advancement over grok, capable of long context understanding up to 128k tokens and advanced reasoning.
Command R+	April 2024	Successor of Command R+ with improved performance for retrieval-augmented generation and tool use, across multiple languages.
Llama 3	April 2024	A family of 8B and 70B parameter models trained on 15T tokens with a focus on data quality, demonstrating state-of-the-art performance on various benchmarks, improved reasoning capabilities.
Mixtral 8x22B	April 2024	A open-weight AI model optimised for performance and efficiency, with capabilities such as fluency in multiple languages, strong mathematics and coding abilities, and precise information recall from large documents.
CodeGemma	April 2024	Open code models based on Gemma models by further training on over 500 billion tokens of primarily code.
RecurrentGemma	April 2024	Based on Griffin, uses a combination of linear recurrences and local attention instead of global attention to model long sequences efficiently.
Rho-1	April 2024	Introduces Selective Language Modelling that optimizes the loss only on tokens that align with a desired distribution, utilizing a reference model to score and select tokens.
Phi-3	April 2024	A series of language models trained on heavily filtered web and synthetic data set, achieving performance comparable to much larger models like Mixtral 8x7B and GPT-3.5.
Open ELM	April 2024	A fully open language model designed to enhance accuracy while using fewer parameters and pre-training tokens. Utilizes a layer-wise scaling strategy to allocate smaller dimensions in early layers, expanding in later layers.
H2O Danube2 1.8B	April 2024	An updated version of the original H2O-Danube model, with improvements including removal of sliding window attention, changes to the tokenizer, and adjustments to the training data, resulting in significant performance enhancements.
MAmmoTH 2	May 2024	LLMs fine tuned on a dataset curated through the proposed paradigm that efficiently harvest 10M naturally existing instruction data from the pre-training web corpus to enhance LLM reasoning. It involves recalling relevant documents, extracting instruction-response pairs, and refining the extracted pairs using open-source LLMs.
Granite Code Models	May 2024	A family of code models ranging from 3B to 34B trained on 3.5-4.5T tokens of code written in 116 programming languages.
Codestral 22B	May 2024	An open-weight model designed for code generation tasks, trained on over 80 programming languages, and licensed under the Mistral AI Non-Production License, allowing developers to use it for research and testing purposes.
Aya 23	May 2024	A family of multilingual language models supporting 23 languages, designed to balance breadth and depth by allocating more capacity to fewer languages during pre-training.
Gemma 2	June 2024	Utilizes interleaving local-global attentions and group-query attention, trained with knowledge distillation instead of next token prediction to achieve competitive performance comparable with larger models.
Orca 3 (Agent Instruct)	June 2024	A fine tuned Mistral-7B through Generative Teaching via synthetic data generated through the proposed AgentInstruct framework, which generates both the prompts and responses, using only raw data sources like text documents and code files as seeds.
Nemotron-4 340B	June 2024	340B models, along with a reward model by Nvidia, suitable for generating synthetic data to train smaller language models, with over 98% of the data used in model alignment being synthetically generated.
Mathstral	July 2024	a 7B model designed for math reasoning and scientific discovery based on Mistral 7B specializing in STEM subjects.
Mistral Nemo	July 2024	A 12B Language Model built in collaboration between Mistral and NVIDIA, featuring a context window of 128K, an efficient tokenizer and trained with quantization awareness, enabling FP8 inference without any performance loss.
Smol LM	July 2024	A family of small models with 135M, 360M, and 1.7B parameters, utilizes Grouped-Query Attention (GQA), embedding tying, and a context length of 2048 tokens, trained on a new open source high-quality dataset.
Llama 3.1	July 2024	A family of multilingual language models ranging from 8B to 405B parameters, trained on a massive dataset of 15T tokens and achieving comparable performance to leading models like GPT-4 on various tasks.
Llama 3.1 - Multimodal Experiments	July 2024	Additional experiments of adding multimodal capabilities to Llama3.
Mistral Large 2	July 2024	A 123B model, offers significant improvements in code generation, mathematics, and reasoning capabilities, advanced function calling, a 128k context window, and supports dozens of languages and over 80 coding languages.
Minitron	July 2024	Prunes an existing Nemotron model and re-trains it with a fraction of the original training data, achieving compression factors of 2-4×, compute cost savings of up to 40×, and improved performance on various language modeling tasks.
H2O Danube 3	July 2024	A series of 4B and 500M language models, trained on high-quality Web data in three stages with different data mixes before being fine-tuned for chat version.
LLM Compiler	July 2024	A suite of pre-trained models designed for code optimization tasks, built upon Code Llama, with two sizes (7B and 13B), trained on LLVM-IR and assembly code to optimize compiler intermediate representations, assemble/disassemble, and achieve high accuracy in optimizing code size and disassembling from x86_64 and ARM assembly back into LLVM-IR.
Apple Intelligence Foundation Language Models	July 2024	Two foundation language models, AFM-on-device (a ~3 B parameter model) and AFM-server (a larger server-based model), designed to power Apple Intelligence features efficiently, accurately, and responsibly, with a focus on Responsible AI principles that prioritize user empowerment, representation, design care, and privacy protection.
Hermes 3	August 2024	Neutrally generalist instruct and tool use models, created by fine-tuning Llama 3.1 models with strong reasoning and creative abilities, and are designed to follow prompts neutrally without moral judgment or personal opinions.
Smol LM v0.2	August 2024	An advancement over SmolLM, better at staying on topic and responding appropriately to standard prompts, such as greetings and questions about their role as AI assistants.
Phi-3.5	August 2024	A family of models consisting of three variants - MoE (16x3.8B), mini (3.8B), and vision (4.2B) - which are lightweight, multilingual, and trained on synthetic and filtered publicly available documents - with a focus on very high-quality, reasoning dense data.
Minitron Approach in Practice	August 2024	Applies the minitron approach to Llama 3.1 8B and Mistral-Nemo 12B, additionally applies teacher correction to align with the new data distribution.
OLMoE	September 2024	An open source language model based on sparse Mixture-of-Experts architecture with 7B parameters, out of which only 1B parameters are active per input token. Conducted extensive experiments on MoE training, analyzing routing strategies, expert specialization, and the impact of design choices like routing algorithms and expert size.
o1	September 2024	A large language model trained with reinforcement learning to think before answering, producing a long internal chain of thought before responding.
o1-mini	September 2024	A cost-efficient reasoning model, excelling at STEM, especially math and coding , nearly matching the performance of OpenAI o1 on evaluation benchmarks.
Llama 3.1-Nemotron-51B	September 2024	Uses knowledge distillation and NAS to optimize various constraints, resulting in a model that achieves 2.2x faster inference compared to the reference model while maintaining nearly the same accuracy, with an irregular block structure that reduces or prunes attention and FFN layers for better utilization of H100 and improved LLMs for inference.
Mistral Small	September 2024	A 22B model with significant improvements in human alignment, reasoning capabilities, and code over the previous model.
Llama 3.2	September 2024	Small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B).
Ministral	October 2024	3B and 8B models with support up to 128k context length having a special interleaved sliding-window attention pattern for faster and memory-efficient inference.
Quantized Llama 3	October 2024	Optimized versions of the Llama, using techniques like Quantization-Aware Training with LoRA Adapters and SpinQuant, to reduce model size and memory usage while maintaining accuracy and performance, enabling deployment on resource-constrained devices like mobile phones.
Nemotron-Mini-Hindi	October 2024	A bilingual language model based on Nemotron-Mini 4B, specifically trained to improve Hindi and English performance using continuous pre-training on 400B real and synthetic tokens.
Smol LM v2	November 2024	A family of language models (135M, 360M, and 1.7B parameters), trained on 2T, 4T, and 11T tokens respectively from datasets including FineWeb-Edu, DCLM, The Stack, and curated math and coding datasets, with instruction-tuned versions created using Smol Talk dataset and DPO using UltraFeedback.
Command R 7B	December 2024	The smallest, fastest, and final model in the R series of enterprise-focused LLMs. It offers a context length of 128k and delivers a powerful combination of multilingual support, citation verified retrieval-augmented generation (RAG), reasoning, tool use, and agentic behavior.
ModernBERT	December 2024	Modernized encoder-only transformer model trained on 2 trillion tokens with a native 8192 sequence length, incorporating architectural improvements like GeGLU activations, RoPE embeddings, alternating attention, and unpadding, resulting in state-of-the-art performance across diverse classification and retrieval tasks (including code) and superior inference speed and memory efficiency compared to existing encoder models.

Multi Modal Language Models

Paper	Date	Description
Florence	November 2021	A computer vision foundation model that can be adapted to various tasks by expanding representations from coarse (scene) to fine (object), static (images) to dynamic (videos), and RGB to multiple modalities.
BLIP	February 2022	A Vision-Language Pre-training (VLP) framework that introduces Multimodal mixture of Encoder-Decoder (MED) and Captioning and Filtering (CapFilt), a new dataset bootstrapping method for learning from noisy image-text pairs.
Flamingo	April 2022	Visual Language Models enabling seamless handling of interleaved visual and textual data, and facilitating few-shot learning on large-scale web corpora.
PaLI	September 2022	A joint language-vision model that generates multilingual text based on visual and textual inputs, trained using large pre-trained encoder-decoder language models and Vision Transformers, specifically mT5 and ViT-e.
BLIP 2	January 2023	A Vision-Language Pre-training (VLP) framework that proposes Q-Former, a trainable module to bridge the gap between a frozen image encoder and a frozen LLM to bootstrap vision-language pre-training.
LLaVA 1	April 2023	A large multimodal model connecting CLIP and Vicuna trained end-to-end on instruction-following data generated through GPT-4 from image-text pairs.
PaLI-X	May 2023	A multilingual vision and language model with scaled-up components, specifically ViT-22 B and UL2 32B, exhibits emergent properties such as complex counting and multilingual object detection, and demonstrates improved performance across various tasks.
InstructBLIP	May 2023	Introduces instruction-aware Query Transformer to extract informative features tailored to the given instruction to study vision-language instruction tuning based on the pretrained BLIP-2 models.
Idefics	June 2023	9B and 80B multimodal models trained on Obelics, an open web-scale dataset of interleaved image-text documents, curated in this work.
GPT-4V	September 2023	A multimodal model that combines text and vision capabilities, allowing users to instruct it to analyze image inputs.
PaLI-3	October 2023	A 5B vision language model, built upon a 2B SigLIP Vision Model and UL2 3B Language Model outperforms larger models on various benchmarks and achieves SOTA on several video QA benchmarks despite not being pretrained on any video data.
LLaVA 1.5	October 2023	An enhanced version of the LLaVA model that incorporates a CLIP-ViT-L-336px with an MLP projection and academic-task-oriented VQA data to set new benchmarks in large multimodal models (LMM) research.
Florence-2	November 2023	A vision foundation model with a unified, prompt-based representation for a variety of computer vision and vision-language tasks.
CogVLM	November 2023	Bridges the gap between the frozen pretrained language model and image encoder by a trainable visual expert module in the attention and FFN layers.
Gemini 1.0	December 2023	A family of highly capable multi-modal models, trained jointly across image, audio, video, and text data for the purpose of building a model with strong generalist capabilities across modalities.
MoE-LLaVA	January 2024	A MoE-based sparse LVLM framework that activates only the top-k experts through routers during deployment, maintaining computational efficiency while achieving comparable performance to larger models.
LLaVA 1.6	January 2024	An improved version of a LLaVA 1.5 with enhanced reasoning, OCR, and world knowledge capabilities, featuring increased image resolution
Gemini 1.5 Pro	February 2024	A highly compute-efficient multimodal mixture-of-experts model that excels in long-context retrieval tasks and understanding across text, video, and audio modalities.
Claude 3	March 2024	A family of VLMs consisting of Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus, sets new industry standards for cognitive tasks, offering varying levels of intelligence, speed, and cost-efficiency.
MM1	March 2024	A multimodal llm that combines a ViT-H image encoder with 378x378px resolution, pretrained on a data mix of image-text documents and text-only documents, scaled up to 3B, 7B, and 30B parameters for enhanced performance across various tasks.
Grok 1.5 V	April 2024	The first multimodal model in the grok series.
Idefics2	April 2024	Improvement upon Idefics1 with enhanced OCR capabilities, simplified architecture, and better pre-trained backbones, trained on a mixture of openly available datasets and fine-tuned on task-oriented data.
Phi 3 Vision	May 2024	First multimodal model in the Phi family, bringing the ability to reason over images and extract and reason over text from images.
An Introduction to Vision-Language Modeling	May 2024	Provides a comprehensive introduction to VLMs, covering their definition, functionality, training methods, and evaluation approaches, aiming to help researchers and practitioners enter the field and advance the development of VLMs for various applications.
GPT-4o	May 2024	An omni model accepting and generating various types of inputs and outputs, including text, audio, images, and video.
Gemini 1.5 Flash	May 2024	A more lightweight variant of the Gemini 1.5 pro, designed for efficiency with minimal regression in quality, making it suitable for applications where compute resources are limited.
Chameleon	May 2024	A family of early-fusion token-based mixed-modal models capable of understanding and generating images and text in any arbitrary sequence.
Claude 3.5 Sonnet	June 2024	Surpasses previous versions and competitors in intelligence, speed, and cost-efficiency, excelling in graduate-level reasoning, undergraduate-level knowledge, coding proficiency, and visual reasoning.
Pali Gemma	July 2024	Combines SigLIP vision model and the Gemma language model and follows the PaLI-3 training recipe to achieve strong performance on various vision-language tasks.
GPT-4o mini	July 2024	A cost-efficient small model that outperforms GPT-4 on chat preferences, enabling a broad range of tasks with low latency and supporting text, vision, and multimodal inputs and outputs.
Grok 2	August 2024	A frontier language model with state-of-the-art capabilities in chat, coding, and reasoning on par with Claude 3.5 Sonnet and GPT-4-Turbo.
BLIP-3 (xGen-MM)	August 2024	A comprehensive system for developing Large Multimodal Models, comprising curated datasets, training recipes, model architectures, and pre-trained models that demonstrate strong in-context learning capabilities and competitive performance on various tasks.
Idefics 3	August 2024	A VLM based on Llama 3.1 and SigLIP-SO400M trained efficiently, using only open datasets and a straightforward pipeline, significantly outperforming in document understanding tasks.
CogVLM2	August 2024	A family of visual language models that enables image and video understanding with improved training recipes, exploring enhanced vision-language fusion, higher input resolution, and broader modalities and applications.
Eagle	August 2024	Provides an extensive exploration of the design space for MLLMs using a mixture of vision encoders and resolutions, and reveals several underlying principles common to various existing strategies, leading to a streamlined yet effective design approach.
Pixtral	September 2024	A 12B parameter natively multimodal vision-language model, trained with interleaved image and text data demonstrating strong performance on multimodal tasks, and excels in instruction following.
NVLM	September 2024	A family of multimodal large language models, provides a comparison between decoder-only multimodal LLMs and cross-attention based models and proposes a hybrid architecture, it further introduces a 1-D title-tagging design for tile-based dynamic high resolution images.
Molmo	September 2024	A family of open-weight vision-language models that achieve state-of-the-art performance by leveraging a novel, human-annotated image caption dataset called PixMo.
MM-1.5	September 2024	A family of multimodal large language models designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and multi-image reasoning, achieved through a data-centric approach involving diverse data mixtures, And specialized variants for video and mobile UI understanding.
Mississippi	October 2024	A collection of small, efficient, open-source vision-language models built on top of Danube, trained on 37 million image-text pairs, specifically designed to perform well on document analysis and OCR tasks while maintaining strong performance on general vision-language benchmarks.
Claude 3.5 Haiku	October 2024	A fast and affordable language model that excels in tasks such as coding, reasoning, and content creation.
Smol VLM	November 2024	A 2B vision-language model, built using a modified Idefics3 architecture with a smaller language backbone (SmolLM2 1.7B), aggressive pixel shuffle compression, 384x384 image patches, and a shape-optimized SigLIP vision backbone, featuring a 16k token context window.

Retrieval and Representation Learning

Paper	Date	Description
SimCLR	February 2020	A simplified framework for contrastive learning that optimizes data augmentation composition, introduces learnable nonlinear transformations, and leverages larger batch sizes and more training steps.
Dense Passage Retriever	April 2020	Shows that retrieval can be practically implemented using dense representations alone, where embeddings are learned from a small number of questions and passages by a simple dual encoder framework.
ColBERT	April 2020	Introduces a late interaction architecture that adapts deep LMs (in particular, BERT) for efficient retrieval.
SimCLRv2	June 2020	A Semi-supervised learning framework which uses unsupervised pre training followed by supervised fine-tuning and distillation with unlabeled examples.
CLIP	February 2021	A vision system that learns image representations from raw text-image pairs through pre-training, enabling zero-shot transfer to various downstream tasks.
ColBERTv2	December 2021	Couples an aggressive residual compression mechanism with a denoised supervision strategy to simultaneously improve the quality and space footprint of late interaction.
Matryoshka Representation Learning	May 2022	Encodes information at different granularities and allows a flexible representation that can adapt to multiple downstream tasks with varying computational resources using a single embedding.
E5	December 2022	A family of text embeddings trained in a contrastive manner with weak supervision signals from a curated large-scale text pair dataset CCPairs.
SigLip	March 2023	A simple pairwise Sigmoid loss function for Language-Image Pre-training that operates solely on image-text pairs, allowing for larger batch sizes and better performance at smaller batch sizes.
Jina Embeddings v1	July 2023	Contrastively fine tuned T5 encoder on curated high quality pairwise and triplet data specifically to sensitize the models to distinguish negations of statements from confirming statements.
Jina Embeddings v2	October 2023	An open-source text embedding model capable of accommodating up to 8192 tokens, trained by pre-training a modified BERT from scratch before fine tuning for embeddings objectives.
SynCLR	December 2023	A visual representation learning method that leverages generative models to synthesize large-scale curated datasets without relying on any real data.
E5 Mistral 7B	December 2023	Leverages proprietary LLMs to generate diverse synthetic data to fine tune open-source decoder-only LLMs for hundreds of thousands of text embedding tasks.
Nomic Embed Text v1	February 2024	A 137M parameter, open-source English text embedding model with an 8192 context length that outperforms OpenAI's models on both short and long-context tasks.
Nomic Embed Text v1.5	February 2024	An advanced text embedding model that utilizes Matryoshka Representation Learning to offer flexible embedding sizes with minimal performance trade-offs
Jina Bilingual Embeddings	February 2024	A suite of bilingual text embedding models that support up to 8192 tokens, trained by pre-training a modified bilingual BERT from scratch before fine tuning for embeddings objectives.
Jina Reranker	February 2024	A neural reranking model that enhances search and RAG systems by reordering retrieved documents for better alignment with search query terms.
Gecko	March 2024	A 1.2B versatile text embedding model achieving strong retrieval performance by distilling knowledge from LLMs into a retriever.
NV Embed	May 2024	Introduces architectural innovations and training recipe to significantly enhance LLMs performance in general-purpose text embedding tasks.
Nomic Embed Vision v1 and v1.5	June 2024	Aligns a Vision Encoder with the existing text encoders without destroying the downstream performance of the text encoder, to attain a unified multimodal latent space.
ColPali	June 2024	A retrieval model based on PaliGemma to produce high-quality contextualized embeddings solely from images of document pages, and employees late interaction allowing for efficient and effective visually rich document retrieval.
Jina Reranker v2	June 2024	Builds upon Jina Reranker v1 by adding multilingual support, function-calling capabilities, structured data querying, code retrieval, and ultra-fast inference.
E5-V	July 2024	A framework that adapts Multimodal Large Language Models for achieving universal multimodal embeddings by leveraging prompts and single modality training on text pairs, which demonstrates strong performance in multimodal embeddings without fine-tuning and eliminates the need for costly multimodal training data collection.
Matryoshka Adaptor	July 2024	A framework designed for the customization of LLM embeddings, facilitating substantial dimensionality reduction while maintaining comparable performance levels.
Jina Embeddings v3	September 2024	A text embedding model with 570 million parameters that supports long-context retrieval tasks up to 8192 tokens, includes LoRA adapters for various NLP tasks, and allows flexible output dimension reduction from 1024 down to 32 using Matryoshka Representation Learning.

Parameter Efficient Fine Tuning

Paper	Date	Description
LoRA	July 2021	Introduces trainable rank decomposition matrices into each layer of a pre-trained Transformer model, significantly reducing the number of trainable parameters for downstream tasks.
DyLoRA	October 2022	Allows for flexible rank size by randomly truncating low-rank matrices during training, enabling adaptation to different rank values without retraining.
AdaLoRA	March 2023	Dynamically allocates a parameter budget based on an importance metric to prune less important singular values during training.
QLoRA	May 2023	Allows efficient training of large models on limited GPU memory, through innovations like 4-bit NormalFloat (NF4), double quantization, and paged optimizers.
LoRA-FA	August 2023	Freezes one of the low-rank matrices and only trains a scaling vector for the other, further reducing the number of trainable parameters compared to standard LoRA.
Delta-LoRA	September 2023	Utilizes the delta of the low-rank matrix updates to refine the pre-trained weights directly, removing the Dropout layer for accurate backpropagation.
LongLoRA	September 2023	Enables context extension for large language models, achieving significant computation savings through sparse local attention and parameter-efficient fine-tuning.
VeRA	October 2023	Utilizes frozen, shared random matrices across all layers and trains scaling vectors to adapt those matrices for each layer, reducing the number of trainable parameters compared to LoRA.
LoRA+	February 2024	Enhances LoRA by setting different learning rates for the A and B matrices based on a fixed ratio, promoting better feature learning and improved performance.
MoRA	May 2024	Introduces a square matrix and non-parameterized operators to achieve high-rank updating with the same number of trainable parameters as LoRA, improving knowledge memorization capabilities.
DoRA	May 2024	Decomposes the high-rank LoRA matrix into multiple single-rank components, allowing dynamic pruning of less important components during training for a more efficient parameter budget allocation.

LLM Evaluation

Paper	Date	Description
Prometheus	October 2023	A 13B fully open source evaluation LLM trained on Feedback Collection curated using GPT-4 (in this work).
Prometheus 2	May 2024	7B & 8x7B evaluation LLMs that score high correlations with both human evaluators and proprietary LM-based judges on both direct assessment and pairwise ranking, obtained by merging Mistral models trained on Feedback Collection and Preference Collection (curated in this work.

Compression, Pruning, Quantization

Paper	Date	Description
LLMLingua	October 2023	A novel coarse-to-fine prompt compression method, incorporating a budget controller, an iterative token-level compression algorithm, and distribution alignment, achieving up to 20x compression with minimal performance loss.
LongLLMLingua	October 2023	A novel approach for prompt compression to enhance performance in long context scenarios using question-aware compression and document reordering.
LLMLingua2	March 2024	A novel approach to task-agnostic prompt compression, aiming to enhance generalizability, using data distillation and leveraging a Transformer encoder for token classification.

Vision Transformers

Paper	Date	Description
Vision Transformer	October 2020	Images are segmented into patches, which are treated as tokens and a sequence of linear embeddings of these patches are input to a Transformer
DeiT	December 2020	A convolution-free vision transformer that uses a teacher-student strategy with attention-based distillation tokens.
Swin Transformer	March 2021	A hierarchical vision transformer that uses shifted windows to addresses the challenges of adapting the transformer model to computer vision.
Convolutional Vision Transformer	March 2021	Improves Vision Transformer (ViT) in performance and efficiency by introducing convolutions, to yield the best of both designs.
LeViT	April 2021	A hybrid neural network built upon the ViT architecture and DeiT training method, for fast inference image classification.
DINO	April 2021	Investigates whether self-supervised learning provides new properties to Vision Transformer that stand out compared to convolutional networks and finds that self-supervised ViT features contain explicit information about the semantic segmentation of an image, and are also excellent k-NN classifiers.
BEiT	June 2021	Utilizes a masked image modeling task inspired by BERT in, involving image patches and visual tokens to pretrain vision Transformers.
MobileViT	October 2021	A lightweight vision transformer designed for mobile devices, effectively combining the strengths of CNNs and ViTs.
Masked AutoEncoder	November 2021	An encoder-decoder architecture that reconstructs input images by masking random patches and leveraging a high proportion of masking for self-supervision.
DINOv2	April 2022	Demonstrates that existing self-supervised pre-training methods can produce general-purpose visual features by training on curated data from diverse sources, and proposes a new approach that combines techniques to scale pre-training with larger models and datasets.
MaxViT	April 2022	Introduces multi-axis attention, allowing global-local spatial interactions on arbitrary input resolutions with only linear complexity.
Swin Transformer V2	April 2022	A successor to Swin Transformer, addressing challenges like training stability, resolution gaps, and labeled data scarcity.
EfficientFormer	June 2022	Revisits the design principles of ViT and its variants through latency analysis and identifies inefficient designs and operators in ViT to propose a new dimension consistent design paradigm for vision transformers and a simple yet effective latency-driven slimming method to optimize for inference speed.
FastViT	March 2023	A hybrid vision transformer architecture featuring a novel token mixing operator called RepMixer, which significantly improves model efficiency.
Efficient Vit	May 2023	Employs a single memory-bound MHSA between efficient FFN layers, improves memory efficiency while enhancing channel communication.
SoViT	May 2023	A shape-optimized vision transformer that achieves competitive results with models twice its size, while being pre-trained with an equivalent amount of compute.

Convolutional Neural Networks

Paper	Date	Description
Lenet	December 1998	Introduced Convolutions.
Alex Net	September 2012	Introduced ReLU activation and Dropout to CNNs. Winner ILSVRC 2012.
VGG	September 2014	Used large number of filters of small size in each layer to learn complex features. Achieved SOTA in ILSVRC 2014.
Inception Net	September 2014	Introduced Inception Modules consisting of multiple parallel convolutional layers, designed to recognize different features at multiple scales.
Inception Net v2 / Inception Net v3	December 2015	Design Optimizations of the Inception Modules which improved performance and accuracy.
Res Net	December 2015	Introduced residual connections, which are shortcuts that bypass one or more layers in the network. Winner ILSVRC 2015.
Inception Net v4 / Inception ResNet	February 2016	Hybrid approach combining Inception Net and ResNet.
Dense Net	August 2016	Each layer receives input from all the previous layers, creating a dense network of connections between the layers, allowing to learn more diverse features.
Xception	October 2016	Based on InceptionV3 but uses depthwise separable convolutions instead on inception modules.
Res Next	November 2016	Built over ResNet, introduces the concept of grouped convolutions, where the filters in a convolutional layer are divided into multiple groups.
Mobile Net V1	April 2017	Uses depthwise separable convolutions to reduce the number of parameters and computation required.
Mobile Net V2	January 2018	Built upon the MobileNetv1 architecture, uses inverted residuals and linear bottlenecks.
Mobile Net V3	May 2019	Uses AutoML to find the best possible neural network architecture for a given problem.
Efficient Net	May 2019	Uses a compound scaling method to scale the network's depth, width, and resolution to achieve a high accuracy with a relatively low computational cost.
NF Net	February 2021	An improved class of Normalizer-Free ResNets that implement batch-normalized networks, offer faster training times, and introduce an adaptive gradient clipping technique to overcome instabilities associated with deep ResNets.
Conv Mixer	January 2022	Processes image patches using standard convolutions for mixing spatial and channel dimensions.
ConvNeXt	January 2022	A pure ConvNet model, evolved from standard ResNet design, that competes well with Transformers in accuracy and scalability.
ConvNeXt V2	January 2023	Incorporates a fully convolutional MAE framework and a Global Response Normalization (GRN) layer, boosting performance across multiple benchmarks.
Efficient Net V2	April 2024	A new family of convolutional networks, achieves faster training speed and better parameter efficiency than previous models through neural architecture search and scaling, with progressive learning allowing for improved accuracy on various datasets while training up to 11x faster.
MobileNetV4	April 2024	Features a universally efficient architecture design, including the Universal Inverted Bottleneck (UIB) search block, Mobile MQA attention block, and an optimized neural architecture search recipe, which enables it to achieve high accuracy and efficiency on various mobile devices and accelerators.

Object Detection

Paper	Date	Description
SSD	December 2015	Discretizes bounding box outputs over a span of various scales and aspect ratios per feature map.
Feature Pyramid Network	December 2016	Leverages the inherent multi-scale hierarchy of deep convolutional networks to efficiently construct feature pyramids.
Focal Loss	August 2017	Addresses class imbalance in dense object detectors by down-weighting the loss assigned to well-classified examples.
DETR	May 2020	A novel object detection model that treats object detection as a set prediction problem, eliminating the need for hand-designed components.
OWL ViT	May 2022	Employs Vision Transformers, CLIP-based contrastive pre-training, and bipartite matching loss for open-vocabulary detection, utilizing image-level pre-training, multihead attention pooling, and mosaic image augmentation.
Segment Anything Model	April 2023	Introduces a novel image segmentation task, model, and dataset, aiming to enable prompt-able, zero-shot transfer learning in computer vision.
SAM 2	July 2024	A foundation model towards solving promptable visual segmentation in images and videos based on a simple transformer architecture with streaming memory for real-time video processing.

Region-based Convolutional Neural Networks

Paper	Date	Description
RCNN	November 2013	Uses selective search for region proposals, CNNs for feature extraction, SVM for classification followed by box offset regression.
Fast RCNN	April 2015	Processes entire image through CNN, employs RoI Pooling to extract feature vectors from ROIs, followed by classification and BBox regression.
Faster RCNN	June 2015	A region proposal network (RPN) and a Fast R-CNN detector, collaboratively predict object regions by sharing convolutional features.
Mask RCNN	March 2017	Extends Faster R-CNN to solve instance segmentation tasks, by adding a branch for predicting an object mask in parallel with the existing branch.
Cascade RCNN	December 2017	Proposes a multi-stage approach where detectors are trained with progressively higher IoU thresholds, improving selectivity against false positives.

Document AI

Paper	Date	Description
Table Net	January 2020	An end-to-end deep learning model designed for both table detection and structure recognition.
SPADE	May 2020	Formulates Information Extraction (IE) as a spatial dependency parsing problem.
Layout Parser	March 2021	A library integrating Detectron2, CNN-RNN OCR, layout structures, TensorFlow/PyTorch, and a Model Zoo. The toolkit features Tesseract, Google Cloud Vision for OCR, active learning tools and community platform ensures efficiency and adaptability.
Layout Reader	August 2021	A seq2seq model that accurately predicts reading order, text, and layout information from document images.
Donut	November 2021	An OCR-free Encoder-Decoder Transformer model. The encoder takes in images, decoder takes in prompts & encoded images to generate the required text.
DiT	March 2022	An Image Transformer pre-trained (self-supervised) on document images
Pix2Struct	October 2022	A pretrained image-to-text model designed for visual language understanding, particularly in tasks involving visually-situated language.
Matcha	December 2022	Leverages Pix2Struct, and introduces pretraining tasks focused on math reasoning and chart derendering to improve chart and plot comprehension, enhancing understanding in diverse visual language tasks.
DePlot	December 2022	Built upon MatCha, standardises plot to table task, translating plots into linearized tables (markdown) for processing by LLMs.
UDoP	December 2022	Integrates text, image, and layout information through a Vision-Text-Layout Transformer, enabling unified representation.
GeoLayoutLM	April 2023	Explicitly models geometric relations in pre-training and enhances feature representation.
Nougat	August 2023	A Visual Transformer model that performs an Optical Character Recognition (OCR) task for processing scientific documents into a markup language.
LMDX	September 2023	A methodology to adapt arbitrary LLMs for document information extraction.
DocLLM	January 2024	A lightweight extension to traditional LLMs that focuses on reasoning over visual documents, by incorporating textual semantics and spatial layout without expensive image encoders.

Layout Transformers

Paper	Date	Description
Layout LM	December 2019	Utilises BERT as the backbone, adds two new input embeddings: 2-D position embedding and image embedding (Only for downstream tasks).
LamBERT	February 2020	Utilises RoBERTa as the backbone and adds Layout embeddings along with relative bias.
Layout LM v2	December 2020	Uses a multi-modal Transformer model, to integrate text, layout, and image in the pre-training stage, to learn end-to-end cross-modal interaction.
Structural LM	May 2021	Utilises BERT as the backbone and feeds text, 1D and (2D cell level) embeddings to the transformer model.
Doc Former	June 2021	Encoder-only transformer with a CNN backbone for visual feature extraction, combines text, vision, and spatial features through a multi-modal self-attention layer.
BROS	August 2021	Built upon BERT, encodes relative positions of texts in 2D space and learns from unlabeled documents with area masking strategy.
LiLT	February 2022	Introduced Bi-directional attention complementation mechanism (BiACM) to accomplish the cross-modal interaction of text and layout.
Layout LM V3	April 2022	A unified text-image multimodal Transformer to learn cross-modal representations, that imputs concatenation of text embedding and image embedding.
ERNIE Layout	October 2022	Reorganizes tokens using layout information, combines text and visual embeddings, utilizes multi-modal transformers with spatial aware disentangled attention.

Generative Adversarial Networks

Paper	Date	Description
Generative Adversarial Networks	June 2014	Introduces a framework where, a generative and a discriminative model, are trained simultaneously in a minimax game.
Conditional Generative Adversarial Networks	November 2014	A method for training GANs, enabling the generation based on specific conditions, by feeding them to both the generator and discriminator networks.
Deep Convolutional Generative Adversarial Networks	November 2015	Demonstrates the ability of CNNs for unsupervised learning using specific architectural constraints designed.
Improved GAN	June 2016	Presents a variety of new architectural features and training procedures that can be applied to the generative adversarial networks (GANs) framework.
Wasserstein Generative Adversarial Networks	January 2017	An alternative GAN training algorithm that enhances learning stability, mitigates issues like mode collapse.
Cycle GAN	March 2017	An approach for learning to translate an image from a source domain X to a target domain Y in the absence of paired examples by leveraging adversarial losses and cycle consistency constraints, using two GANs.

Tabular Deep Learning

Paper	Date	Description
Entity Embeddings	April 2016	Maps categorical variables into continuous vector spaces through neural network learning, revealing intrinsic properties.
Wide and Deep Learning	June 2016	Combines memorization of specific patterns with generalization of similarities.
Deep and Cross Network	August 2017	Combines the a novel cross network with deep neural networks (DNNs) to efficiently learn feature interactions without manual feature engineering.
Tab Transformer	December 2020	Employs multi-head attention-based Transformer layers to convert categorical feature embeddings into robust contextual embeddings.
Tabular ResNet	June 2021	An MLP with skip connections.
Feature Tokenizer Transformer	June 2021	Transforms all features (categorical and numerical) to embeddings and applies a stack of Transformer layers to the embeddings.

Datasets

Paper	Date	Description
Obelics	June 2023	An open web-scale filtered dataset of interleaved image-text documents comprising 141M web pages, 353M associated images, and 115B text tokens, extracted from CommonCrawl
Dolma	January 2024	An open corpus of three trillion tokens designed to support language model pretraining research.
Aya Dataset	Februray 2024	A human-curated instruction-following dataset that spans 65 languages, created to bridge the language gap in datasets for natural language processing.
WebSight	March 2024	A synthetic dataset consisting of 2M pairs of HTML codes and their corresponding screenshots, generated through LLMs, aimed to accelerate research for converting a screenshot into a corresponding HTML.
Cosmopedia	March 2024	Synthetic Data containing over 30M files and 25B tokens, generated by Mixtral-8x7B-Instruct-v0., aimed to reproduce the training data for Phi-1.5.
RewardBench	March 2024	A benchmark dataset and code-base designed to evaluate reward models used in RLHF.
Fine Web	May 2024	A large-scale dataset for pretraining LLMs, consisting of 15T tokens, shown to produce better-performing models than other open pretraining datasets.
Cosmopedia v2	July 2024	An enhanced version of Cosmopedia, with a lot of emphasis on prompt optimization.
Docmatix	July 2024	A massive dataset for DocVQA containing 2.4M images, 9.5M question-answer pairs, and 1.3M PDF documents, generated by taking transcriptions from the PDFA OCR dataset and using a Phi-3-small model to generate Q/A pairs.
Pixmo	September 2024	A high-quality dataset of detailed image descriptions collected through speech-based annotations, enabling the creation of more robust and accurate VLMs.
Smol Talk	November 2024	A synthetic instruction-following dataset comprising 1 million samples, built using a fine-tuned LLM on a diverse range of instruction-following datasets and then generating synthetic conversations using various prompts and instructions to improve instruction following, chat, and reasoning capabilities.

LLM Training

Paper	Date	Description
Direct Preference Optimization	December 2023	A stable, performant, and computationally lightweight algorithm that fine-tunes llms to align with human preferences without the need for reinforcement learning, by directly optimizing for the policy best satisfying the preferences with a simple classification objective.
RAFT	March 2024	A training method that enhances the performance of LLMs for open-book in-domain question answering by training them to ignore irrelevant documents, cite verbatim relevant passages, and promote logical reasoning.
RLHF Workflow	May 2024	Provides a detailed recipe for online iterative RLHF and achieves state-of-the-art performance on various benchmarks using fully open-source datasets.
Magpie	June 2024	A self-synthesis method that extracts high-quality instruction data at scale by prompting an aligned LLM with left-side templates, generating 4M instructions and their corresponding responses.
Instruction Pre-Training	June 2024	A framework to augment massive raw corpora with instruction-response pairs enabling supervised multitask pretraining of LMs.
Self-Taught Evaluators	August 2024	An iterative training scheme that uses only synthetically generated preference data, without human annotations, to improve an LLM's ability to judge the quality of model responses by iteratively generating contrasting model outputs, training an LLM-as-a-Judge to produce reasoning traces and judgments, and using the improved predictions in subsequent iterations.
Direct Judgement Preference Optimization	September 2024	Proposes learning through preference optimization to enhance the evaluation capabilities of LLM judges which are trained on three approaches: Chain-of-Thought Critique, Standard Judgement, and Response Deduction across various use cases, including single rating, pairwise comparison, and classification.
LongCite	October 2024	A system comprising LongBench-Cite benchmark, CoF pipeline for generating cited QA instances, LongCite-45k dataset, and LongCite-8B/9B models trained on this dataset to improve the trustworthiness of long-context LLMs by enabling them to generate responses with fine-grained sentence-level citations.
Thought Preference Optimization	October 2024	Iteratively trains LLMs to generate useful "thoughts" that improve response quality by prompting the model to produce thought-response pairs, scoring the responses with a judge model, creating preference pairs from the highest and lowest-scoring responses and their associated thoughts, and then using these pairs with DPO or IRPO loss to optimize the thought generation process while mitigating judge model length bias through score normalization.
Self-Consistency Preference Optimization	November 2024	An unsupervised iterative training method for LLMs that leverages the concept of self-consistency to create preference pairs by selecting the most consistent response as the chosen response and the least consistent one as the rejected response, and then optimizes a weighted loss function that prioritizes pairs with larger vote margins, reflecting the model's confidence in the preference.

Miscellaneous

Paper	Date	Description
ColD Fusion	December 2022	A method enabling the benefits of multitask learning through distributed computation without data sharing and improving model performance.
Are Emergent Abilities of Large Language Models a Mirage?	April 2023	This paper presents an alternative explanation for emergent abilities, i.e. emergent abilities are created by the researcher’s choice of metrics, not fundamental changes in model family behaviour on specific tasks with scale.
Scaling Data-Constrained Language Models	May 2023	This study investigates scaling language models in data-constrained regimes.
RAGAS	September 2023	A framework for reference-free evaluation of RAG systems, assessing the retrieval system's ability to find relevant context, the LLM's faithfulness in using that context, and the overall quality of the generated response.
DSPy	October 2023	A programming model that abstracts LM pipelines as text transformation graphs, i.e. imperative computation graphs where LMs are invoked through declarative modules, optimizing their use through a structured framework of signatures, modules, and teleprompters to automate and enhance text transformation tasks.
An In-depth Look at Gemini's Language Abilities	December 2023	A third-party, objective comparison of the abilities of the OpenAI GPT and Google Gemini models with reproducible code and fully transparent results.
STORM	February 2024	A writing system that addresses the prewriting stage of long-form article generation by researching diverse perspectives, simulating multi-perspective question-asking, and curating information to create an outline, ultimately leading to more organized and comprehensive articles compared to baseline methods.
PromptWizard	May 2024	A framework that leverages LLMs to iteratively synthesize and refine prompts tailored to specific tasks by optimizing both prompt instructions and in-context examples, maximizing model performance.
Monte Carlo Tree Self-refine	June 2024	Integrates LLMs with Monte Carlo Tree Search to enhance performance in complex mathematical reasoning tasks, leveraging systematic exploration and heuristic self-refine mechanisms to improve decision-making frameworks.
Proofread	June 2024	A Gboard feature powered by a server-side LLM, enabling seamless sentence-level and paragraph-level corrections with a single tap.
CriticGPT	June 2024	A model based on GPT-4 trained with RLHF to catch errors in ChatGPT's code output, accepts a question-answer pair as input and outputs a structured critique that highlights potential problems in the answer.
Gemma APS	June 2024	Proposes a scalable, yet accurate, proposition segmentation model by modeling Proposition segmentation as a supervised task by training LLMs on existing annotated datasets.
ShieldGemma	July 2024	A comprehensive suite of LLM-based safety content moderation models ranging from 2B to 27B parameters built upon Gemma2 that provide predictions of safety risks across key harm types (sexually explicit, dangerous content, harassment, hate speech) in both user input and LLM-generated output.
Spreadsheet LLM	July 2024	An efficient encoding method that utilizes SheetCompressor, a framework comprising structural anchor based compression, inverse index translation, and data format aware aggregation, to effectively compress spreadsheets for LLMs, and Chain of Spreadsheet for spreadsheet understanding and spreadsheet QA task.
OmniParser	August 2024	A method for parsing user interface screenshots into structured elements, enhancing the ability of GPT-4V to generate actions grounded in the interface by accurately identifying interactable icons and understanding element semantics.
Reader-LM	September 2024	Small multilingual models specifically trained to generate clean markdown directly from noisy raw HTML, with a context length of up to 256K tokens.
DataGemma	September 2024	A set of models that aims to reduce hallucinations in LLMs by grounding them in the factual data of Google's Data Commons, allowing users to ask questions in natural language and receive responses based on verified information from trusted sources.
GSM-Symbolic	October 2024	Investigates the true mathematical reasoning capabilities of LLMs by introducing GSM-Symbolic, a new benchmark based on symbolic templates, revealing that LLMs exhibit inconsistent performance, struggle with complex questions, and appear to rely on pattern recognition rather than genuine logical reasoning.

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