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Awesome Knowledge Distillation of LLM Papers

<h2 align="center"> A Survey on Knowledge Distillation of Large Language Models </h2> <p align="center"> Xiaohan Xu<sup>1</sup>&nbsp&nbsp Ming Li<sup>2</sup>&nbsp&nbsp Chongyang Tao<sup>3</sup>&nbsp&nbsp Tao Shen<sup>4</sup>&nbsp&nbsp Reynold Cheng<sup>1</sup>&nbsp&nbsp Jinyang Li<sup>1</sup>&nbsp&nbsp Can Xu<sup>5</sup>&nbsp&nbsp Dacheng Tao<sup>6</sup>&nbsp&nbsp Tianyi Zhou<sup>2</sup>&nbsp&nbsp </p> <p align="center"> <sup>1</sup> The University of Hong Kong &nbsp&nbsp <sup>2</sup> University of Maryland &nbsp&nbsp <sup>3</sup> Microsoft &nbsp&nbsp <sup>4</sup> University of Technology Sydney &nbsp&nbsp <sup>5</sup> Peking University &nbsp&nbsp <sup>6</sup> The University of Sydney </p> <div align="center"> <img src="imgs/framework.png" width="700"><br> </div> <br>

A collection of papers related to knowledge distillation of large language models (LLMs). If you want to use LLMs for benefitting your own smaller models training, or use self-generated knowledge to achieve the self-improvement, just take a look at this collection.

We will update this collection every week. Welcome to star ⭐️ this repo to keep track of the updates.

❗️Legal Consideration: It's crucial to note the legal implications of utilizing LLM outputs, such as those from ChatGPT (Restrictions), Llama (License), etc. We strongly advise users to adhere to the terms of use specified by the model providers, such as the restrictions on developing competitive products, and so on.

💡 News

2024-2-20: 📃 We released a survey paper "A Survey on Knowledge Distillation of Large Language Models". Welcome to read and cite it. We are looking forward to your feedback and suggestions.
Update Log
- 2024-3-19: Add 14 papers.

Contributing to This Collection

Feel free to open an issue/PR or e-mail shawnxxh@gmail.com, minglii@umd.edu, hishentao@gmail.com and chongyangtao@gmail.com if you find any missing taxonomies or papers. We will keep updating this collection and survey.

📝 Introduction

KD of LLMs: This survey delves into knowledge distillation (KD) techniques in Large Language Models (LLMs), highlighting KD's crucial role in transferring advanced capabilities from proprietary LLMs like GPT-4 to open-source counterparts such as LLaMA and Mistral. We also explore how KD enables the compression and self-improvement of open-source LLMs by using them as teachers.

KD and Data Augmentation: Crucially, the survey navigates the intricate interplay between data augmentation (DA) and KD, illustrating how DA emerges as a powerful paradigm within the KD framework to bolster LLMs' performance. By leveraging DA to generate context-rich, skill-specific training data, KD transcends traditional boundaries, enabling open-source models to approximate the contextual adeptness, ethical alignment, and deep semantic insights characteristic of their proprietary counterparts.

Taxonomy: Our analysis is meticulously structured around three foundational pillars: algorithm, skill, and verticalization -- providing a comprehensive examination of KD mechanisms, the enhancement of specific cognitive abilities, and their practical implications across diverse fields.

KD Algorithms: For KD algorithms, we categorize it into two principal steps: "Knowledge Elicitation" focusing on eliciting knowledge from teacher LLMs, and "Distillation Algorithms" centered on injecting this knowledge into student models.

<div align="center"> <img src="imgs/knowledge.png" width="600"><br> <em>Figure: An illustration of different knowledge elicitation methods from teacher LLMs.</em> </div> <br>

Skill Distillation: We delve into the enhancement of specific cognitive abilities, such as context following, alignment, agent, NLP task specialization, and multi-modality.

Verticalization Distillation: We explore the practical implications of KD across diverse fields, including law, medical & healthcare, finance, science, and miscellaneous domains.

Note that both Skill Distillation and Verticalization Distillation employ Knowledge Elicitation and Distillation Algorithms in KD Algorithms to achieve their KD. Thus, there are overlaps between them. However, this could also provide different perspectives for the papers.

Why KD of LLMs?

In the era of LLMs, KD of LLMs plays the following crucial roles:

Role	Description	Trend
① Advancing Smaller Models	Transferring advanced capabilities from proprietary LLMs to smaller models, such as open source LLMs or other smaller models.	Most common
② Compression	Compressing open-source LLMs to make them more efficient and practical.	More popular with the prosperity of open-source LLMs
③ Self-Improvement	Refining open-source LLMs' performance by leveraging their own knowledge, i.e. self-knowledge.	New trend to make open-source LLMs more competitive

📒 Table of Contents

KD Algorithms
- Knowledge Elicitation
  - Labeling
  - Expansion
  - Curation
  - Feature
  - Feedback
  - Self-Knowledge
- Distillation Algorithms
Skill Distillation
Verticalization Distillation
- Law
- Medical & Healthcare
- Finance
- Science
- Misc.
Encoder-based KD
Citation

KD Algorithms

Knowledge Elicitation

Labeling

Title	Venue	Date	Code	Data
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering	arXiv	2024-03
Aligning Large and Small Language Models via Chain-of-Thought Reasoning	EACL	2024-03	Github
Divide-or-Conquer? Which Part Should You Distill Your LLM?	arXiv	2024-02
Miko: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery	arXiv	2024-02
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models	arXiv	2024-02	Github
TinyLLM: Learning a Small Student from Multiple Large Language Models	arXiv	2024-02
Mixed Distillation Helps Smaller Language Model Better Reasoning	arXiv	2023-12
Tailoring Self-Rationalizers with Multi-Reward Distillation	arXiv	2023-11	Github	Data
Orca 2: Teaching Small Language Models How to Reason	arXiv	2023-11
Mammoth: Building Math Generalist Models through Hybrid Instruction Tuning	arXiv	2023-09	Github	Data
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization	arXiv	2023-06	Github	Data
Symbolic Chain-of-Thought Distillation: Small Models Can Also "Think" Step-by-Step	ACL	2023-06
Orca: Progressive Learning from Complex Explanation Traces of GPT-4	arXiv	2023-06
Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes	ACL	2023-05	Github	Data
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing	arXiv	2023-05	Github
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data	EMNLP	2023-04	Github	Data
ChatGPT outperforms crowd workers for text-annotation tasks	arXiv	2023-03
Annollm: Making large language models to be better crowdsourced annotators	arXiv	2023-03
GPT-4All: Training an Assistant-Style Chatbot with Large Scale Data Distillation from GPT-3.5-Turbo	-	2023-03	Github
Specializing Smaller Language Models towards Multi-Step Reasoning	arXiv	2023-01
Is GPT-3 a Good Data Annotator?	ACL	2022-12	Github
Large Language Models Are Reasoning Teachers	ACL	2022-12	Github	Data
Teaching Small Language Models to Reason	ACL	2022-12
Explanations from Large Language Models Make Small Reasoners Better	arXiv	2022-10
Want To Reduce Labeling Cost? GPT-3 Can Help	Findings of EMNLP	2021-08

Expansion

Title	Venue	Date	Code	Data
Instruction Fusion: Advancing Prompt Evolution through Hybridization	arXiv	2023-12
An Empirical Study of Instruction-tuning Large Language Models in Chinese	EMNLP	2023-10	Github	Data
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation	EMNLP	2023-10	Github
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct	arXiv	2023-08	Github
Code Llama: Open Foundation Models for Code	arXiv	2023-08	Github
WizardCoder: Empowering Code Large Language Models with Evol-Instruct	ICLR	2023-06	Github
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision	NeurIPS	2023-05	Github	Data
Targeted Data Generation: Finding and Fixing Model Weaknesses	ACL	2023-05	Github
Wizardlm: Empowering large language models to follow complex instructions	ICLR	2023-04	Github	Data <br> Data
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions	arXiv	2023-04	Github	Data
Alpaca: Aligning Language Model with Human Preferences	-	2023-03	Github	Data
Code Alpaca: An Instruction-following LLaMA model for code generation	-	2023-03	Github	Data
Exploring the Impact of Instruction Data Scaling on Large Language Models: An Empirical Study on Real-World Use Cases	arXiv	2023-03	Github	Data
AugGPT: Leveraging ChatGPT for Text Data Augmentation	arXiv	2023-02	Github
Self-instruct: Aligning language model with self generated instructions	ACL	2022-12	Github	Data
Symbolic Knowledge Distillation: from General Language Models to Commonsense Models	NAACL	2021-10	Github	Data

Curation

Title	Venue	Date	Code	Data
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models	arXiv	2024-02
Phi-2: The surprising power of small language models	-	2023-12
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation	arXiv	2023-12
Magicoder: Source Code Is All You Need	arXiv	2023-12	Github	Data <br> Data
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning	arXiv	2023-11	Github	Data <br> Data
Textbooks Are All You Need II: Phi-1.5 Technical Report	arXiv	2023-09
Neural Machine Translation Data Generation and Augmentation using ChatGPT	arXiv	2023-07
Textbooks Are All You Need: A Large-Scale Instructional Text Data Set for Language Models	arXiv	2023-06
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations	arXiv	2023-05	Github	Data
AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation	arXiv	2022-12	Github
SunGen: Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning	ICLR	2022-05	Github
ZeroGen: Efficient Zero-shot Learning via Dataset Generation	EMNLP	2022-02	Github
InPars: Data Augmentation for Information Retrieval using Large Language Models	arXiv	2022-02	Github	Data
Towards Zero-Label Language Learning	arXiv	2021-09

Feature

Title	Venue	Date	Code	Data
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning	EMNLP Findings	2024-02	Github	Data
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models	arXiv	2024-04
Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs	arXiv	2024-03
DB-LLM: Accurate Dual-Binarization for Efficient LLMs	arXiv	2024-02
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation	arXiv	2024-02	Github
DISTILLM: Towards Streamlined Distillation for Large Language Models	arXiv	2024-02	Github
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs	arXiv	2024-02	Github	Data
Revisiting Knowledge Distillation for Autoregressive Language Models	arXiv	2024-02
Knowledge Fusion of Large Language Models	ICLR	2024-01	Github
Improving In-context Learning via Bidirectional Alignment	arXiv	2023-12
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains	NeurIPS	2023-10
Baby Llama: Knowledge Distillation from an Ensemble of Teachers Trained on a Small Dataset with No Performance Penalty	CoNLL	2023-08	Github	Data
f-Divergence Minimization for Sequence-Level Knowledge Distillation	ACL	2023-07	Github	Data
MiniLLM: Knowledge Distillation of Large Language Models	ICLR	2023-06	Github	Data
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes	ICLR	2023-06
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models	arXiv	2023-05	Github	Data
Less is more: Task-aware layer-wise distillation for language model compression	PMLR	2022-10	Github

Feedback

Title	Venue	Date	Code	Data
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning	EMNLP Findings	2024-02	Github	Data
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering	arXiv	2024-03
Evolving Knowledge Distillation with Large Language Models and Active Learning	arXiv	2024-03
Direct Language Model Alignment from Online AI Feedback	arXiv	2024-02
DISTILLM: Towards Streamlined Distillation for Large Language Models	arXiv	2024-02	Github
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint	arXiv	2024-01	Github
Beyond Imitation: Leveraging Fine-grained Quality Signals for Alignment	arXiv	2023-11
Can Language Models Teach Weaker Agents? Teacher Explanations Improve Students via Personalization	ICLR	2023-10	Github
Motif: Intrinsic Motivation from Artificial Intelligence Feedback	ICLR	2023-10	Github
Ultrafeedback: Boosting language models with high-quality feedback	arXiv	2023-10	Github	Data
Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation	EMNLP	2023-10	Github
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment	arXiv	2023-10
Rlaif: Scaling Reinforcement Learning from Human Feedback with AI Feedback	arXiv	2023-09
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct	arXiv	2023-08	Github
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes	ICLR	2023-06
MiniLLM: Knowledge Distillation of Large Language Models	ICLR	2023-06	Github	Data
Language to Rewards for Robotic Skill Synthesis	arXiv	2023-06	Github
Lion: Adversarial Distillation of Closed-Source Large Language Model	EMNLP	2023-05	Github
SelFee: Iterative Self-Revising LLM Empowered by Self-Feedback Generation	arXiv	2023-05
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions	arXiv	2023-04	Github	Data
Reward Design with Language Models	ICLR	2023-03	Github
Consitutional AI: Harmlessness from AI Feedback	arXiv	2022-12

Self-Knowledge

Title	Venue	Date	Code	Data
V-STaR: Training Verifiers for Self-Taught Reasoners	arXiv	2024-02
Self-Rewarding Language Models	arXiv	2024-01	Github
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models	arXiv	2024-01	Github	Data
Kun: Answer Polishment for Chinese Self-Alignment with Instruction Back-Translation	arXiv	2024-01	Github	Data
APT: Adaptive Pruning and Tuning Pretrained Language Models for Efficient Training and Inference	arXiv	2024-01
GRATH: Gradual Self-Truthifying for Large Language Models	arXiv	2024-01
Beyond human data: Scaling self-training for problem-solving with language models	arXiv	2023-12
Self-Knowledge Guided Retrieval Augmentation for Large Language Models	EMNLP Findings	2023-10	Github
RAIN: Your Language Models Can Align Themselves without Finetuning	arXiv	2023-09	Github
Reinforced Self-Training (ReST) for Language Modeling	arXiv	2023-08
Humback: Self-Alignment with Instruction Backtranslation	ICLR	2023-08	Github
Self-Alignment of Large Language Models via Reinforcement Learning from Contrast Distillation	ICLR	2023-07	Github
Self-Improvement of Large Language Models via Reinforcement Learning from Human Feedback	EMNLP	2023-06
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision	NeurIPS	2023-05	Github	Data
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing	arXiv	2023-05	Github
Language Model Self-improvement by Reinforcement Learning Contemplation	arXiv	2023-05
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data	EMNLP	2023-04	Github	Data
Self-instruct: Aligning language model with self generated instructions	ACL	2022-12	Github	Data
Large Language Models Can Self-Improve	EMNLP	2022-10
STaR: Bootstrapping Reasoning With Reasoning	NeurIPS	2022-03	Github

Distillation Algorithms

Supervised Fine-Tuning

Due to the large number of works applying supervised fine-tuning, we only list the most representative ones here.

Title	Venue	Date	Code	Data
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering	arXiv	2024-03
Aligning Large and Small Language Models via Chain-of-Thought Reasoning	EACL	2024-03	Github
Divide-or-Conquer? Which Part Should You Distill Your LLM?	arXiv	2024-02
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models	arXiv	2024-02
Orca 2: Teaching Small Language Models How to Reason	arXiv	2023-11
TinyLLM: Learning a Small Student from Multiple Large Language Models	arXiv	2024-02
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct	arXiv	2023-08	Github
Orca: Progressive Learning from Complex Explanation Traces of GPT-4	arXiv	2023-06
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions	arXiv	2023-04	Github	Data
Wizardlm: Empowering large language models to follow complex instructions	ICLR	2023-04	Github	Data <br> Data
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data	EMNLP	2023-04	Github	Data
Alpaca: Aligning Language Model with Human Preferences	-	2023-03	Github	Data
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality*	-	2023-03	Github	Data
Self-instruct: Aligning language model with self generated instructions	ACL	2022-12	Github	Data
Large Language Models Can Self-Improve	EMNLP	2022-10
STaR: Bootstrapping Reasoning With Reasoning	NeurIPS	2022-03	Github

Divergence and Similarity

Title	Venue	Date	Code	Data
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt Tuning	EMNLP Findings	2024-02	Github	Data
Rethinking Kullback-Leibler Divergence in Knowledge Distillation for Large Language Models	arXiv	2024-04
Weight-Inherited Distillation for Task-Agnostic BERT Compression	NAACL	2024-03	Github
BitDistiller: Unleashing the Potential of Sub-4-Bit LLMs via Self-Distillation	arXiv	2024-02	Github
DISTILLM: Towards Streamlined Distillation for Large Language Models	arXiv	2024-02	Github
Towards Cross-Tokenizer Distillation: the Universal Logit Distillation Loss for LLMs	arXiv	2024-02	Github	Data
Revisiting Knowledge Distillation for Autoregressive Language Models	arXiv	2024-02
Knowledge Distillation for Closed-Source Language Models	arXiv	2024-01
Knowledge Fusion of Large Language Models	ICLR	2024-01	Github
Improving In-context Learning via Bidirectional Alignment	arXiv	2023-12
Towards the Fundamental Limits of Knowledge Transfer over Finite Domains	NeurIPS	2023-10
Baby Llama: Knowledge Distillation from an Ensemble of Teachers Trained on a Small Dataset with No Performance Penalty	CoNLL	2023-08	Github	Data
f-Divergence Minimization for Sequence-Level Knowledge Distillation	ACL	2023-07	Github	Data
f-Divergence Minimization for Sequence-Level Knowledge Distillation	ACL	2023-07	Github	Data
MiniLLM: Knowledge Distillation of Large Language Models	ICLR	2023-06	Github	Data
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes	ICLR	2023-06
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models	arXiv	2023-05	Github	Data
Less is more: Task-aware layer-wise distillation for language model compression	PMLR	2022-10	Github
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter	NeurIPS	2019-10

Reinforcement Learning

Title	Venue	Date	Code	Data
Direct Language Model Alignment from Online AI Feedback	arXiv	2024-02
Improving Large Language Models via Fine-grained Reinforcement Learning with Minimum Editing Constraint	arXiv	2024-01	Github
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models	CoRL	2023-11
Motif: Intrinsic Motivation from Artificial Intelligence Feedback	ICLR	2023-10	Github
Ultrafeedback: Boosting language models with high-quality feedback	arXiv	2023-10	Github	Data
Eureka: Human-Level Reward Design via Coding Large Language Models	arXiv	2023-10	Github
Rlaif: Scaling Reinforcement Learning from Human Feedback with AI Feedback	arXiv	2023-09
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct	arXiv	2023-08	Github
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes	ICLR	2023-06
Aligning Large Language Models through Synthetic Feedback	EMNLP	2023-05	Github	Data
Language Model Self-improvement by Reinforcement Learning Contemplation	arXiv	2023-05
Consitutional AI: Harmlessness from AI Feedback	arXiv	2022-12

Rank Optimization

Title	Venue	Date	Code	Data
Evidence-Focused Fact Summarization for Knowledge-Augmented Zero-Shot Question Answering	arXiv	2024-03
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models	arXiv	2024-02	Github
Self-Rewarding Language Models	arXiv	2024-01	Github
Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models	arXiv	2024-01	Github	Data
Zephyr: Direct Distillation of Language Model Alignment	arXiv	2023-10	Github	Data
CycleAlign: Iterative Distillation from Black-box LLM to White-box Models for Better Human Alignment	arXiv	2023-10

Skill Distillation

Context Following

Instruction Following

Title	Venue	Date	Code	Data
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models	arXiv	2024-02
Revisiting Knowledge Distillation for Autoregressive Language Models	arXiv	2024-02
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning	arXiv	2024-02	Github	Data
Phi-2: The surprising power of small language models	-	2023-12
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning	ICLR	2023-12	Github	Data
MUFFIN: Curating Multi-Faceted Instructions for Improving Instruction-Following	arXiv	2023-12	Github	Data
Instruction Fusion: Advancing Prompt Evolution through Hybridization	arXiv	2023-12
Orca 2: Teaching Small Language Models How to Reason	arXiv	2023-11
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning	NIPS Workshop	2023-10	Github	Data
Textbooks Are All You Need II: Phi-1.5 Technical Report	arXiv	2023-09
Orca: Progressive Learning from Complex Explanation Traces of GPT-4	arXiv	2023-06
Textbooks Are All You Need: A Large-Scale Instructional Text Data Set for Language Models	arXiv	2023-06
SelFee: Iterative Self-Revising LLM Empowered by Self-Feedback Generation	arXiv	2023-05
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts	arXiv	2023-05	Github	Data
LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions	arXiv	2023-04	Github	Data
Wizardlm: Empowering large language models to follow complex instructions	ICLR	2023-04	Github	Data <br> Data
Koala: A Dialogue Model for Academic Research	-	2023-04	Github	Data
Alpaca: Aligning Language Model with Human Preferences	-	2023-03	Github	Data
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality*	-	2023-03	Github	Data
Self-instruct: Aligning language model with self generated instructions	ACL	2022-12	Github	Data

Multi-turn Dialogue

Title	Venue	Date	Code	Data
Zephyr: Direct Distillation of LM Alignment	arXiv	2023-10	Github	Data
OPENCHAT: ADVANCING OPEN-SOURCE LANGUAGE MODELS WITH MIXED-QUALITY DATA	ICLR	2023-09	Github	Data
Enhancing Chat Language Models by Scaling High-quality Instructional Conversations	arXiv	2023-05	Github	Data
Baize: An Open-Source Chat Model with Parameter-Efficient Tuning on Self-Chat Data	EMNLP	2023-04	Github	Data
Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality*	-	2023-03	Github	Data

RAG Capability

Title	Venue	Date	Code	Data
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection	NIPS	2023-10	Github	Data
SAIL: Search-Augmented Instruction Learning	arXiv	2023-05	Github	Data
Knowledge-Augmented Reasoning Distillation for Small Language Models in Knowledge-Intensive Tasks	NIPS	2023-05	Github	Data

Alignment

Thinking Pattern

Title	Venue	Date	Code	Data
Aligning Large and Small Language Models via Chain-of-Thought Reasoning	EACL	2024-03	Github
Divide-or-Conquer? Which Part Should You Distill Your LLM?	arXiv	2024-02
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning	arXiv	2024-02	Github	Data
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements	arXiv	2024-02	Github	Data
Knowledgeable Preference Alignment for LLMs in Domain-specific Question Answering	arXiv	2023-11	Github
Orca 2: Teaching Small Language Models How to Reason	arXiv	2023-11
Reflection-Tuning: Data Recycling Improves LLM Instruction-Tuning	NIPS Workshop	2023-10	Github	Data
Orca: Progressive Learning from Complex Explanation Traces of GPT-4	arXiv	2023-06
SelFee: Iterative Self-Revising LLM Empowered by Self-Feedback Generation	arXiv	2023-05

Preference

Title	Venue	Date	Code	Data
Ultrafeedback: Boosting language models with high-quality feedback	arXiv	2023-10	Github	Data
Zephyr: Direct Distillation of LM Alignment	arXiv	2023-10	Github	Data
Rlaif: Scaling Reinforcement Learning from Human Feedback with AI Feedback	arXiv	2023-09
OPENCHAT: ADVANCING OPEN-SOURCE LANGUAGE MODELS WITH MIXED-QUALITY DATA	ICLR	2023-09	Github	Data
RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment	arXiv	2023-07	Github
Aligning Large Language Models through Synthetic Feedbacks	EMNLP	2023-05	Github	Data
Reward Design with Language Models	ICLR	2023-03	Github
Training Language Models with Language Feedback at Scale	arXiv	2023-03
Constitutional AI: Harmlessness from AI Feedback	arXiv	2022-12

Value

Title	Venue	Date	Code	Data
Ultrafeedback: Boosting language models with high-quality feedback	arXiv	2023-10	Github	Data
RLCD: Reinforcement Learning from Contrast Distillation for Language Model Alignment	arXiv	2023-07	Github
Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision	NeurIPS	2023-05	Github	Data
Training Socially Aligned Language Models on Simulated Social Interactions	arXiv	2023-05
Constitutional AI: Harmlessness from AI Feedback	arXiv	2022-12

Agent

Tool Using

Title	Venue	Date	Code	Data
Toolformer: Language Models Can Teach Themselves to Use Tools	arXiv	2023-02
Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT	arXiv	2023-04	Github	Data
Gorilla: Large Language Model Connected with Massive APIs	arXiv	2023-05	Github	Data
GPT4Tools: Teaching Large Language Model to Use Tools via Self-instruction	arXiv	2023-05	Github	Data
ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases	arXiv	2023-06	Github	Data
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs	arXiv	2023-07	Github	Data
Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum	arXiv	2023-08	Github
CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets	arXiv	2023-09	Github
MLLM-Tool: A Multimodal Large Language Model For Tool Agent Learning	arXiv	2024-01	Github	Data
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent	arXiv	2024-01	Github
EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction	arXiv	2024-01	Github

Planning

Title	Venue	Date	Code	Data
AUTOACT: Automatic Agent Learning from Scratch via Self-Planning	arXiv	2024-01	Github
Lumos: Learning Agents with Unified Data, Modular Design, and Open-Source LLMs	arXiv	2023-11	Github	Data
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Systems	arXiv	2023-11
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld	arXiv	2023-11
Accelerating Reinforcement Learning of Robotic Manipulations via Feedback from Large Language Models	CoRL	2023-11
Motif: Intrinsic Motivation from Artificial Intelligence Feedback	ICLR	2023-10	Github
FireAct: Toward Language Agent Fine-tuning	arXiv	2023-10	Github	Data
AgentTuning: Enabling Generalized Agent Abilities for LLMs	arXiv	2023-10	Github
Eureka: Human-Level Reward Design via Coding Large Language Models	arXiv	2023-10	Github
Language Instructed Reinforcement Learning for Human-AI Coordination	PMLR	2023-04
Guiding Pretraining in Reinforcement Learning with Large Language Models	PMLR	2023-02
Distilling Internet-Scale Vision-Language Models into Embodied Agents	ICML	2023-01

NLP Task Specialization

NLU

Title	Venue	Date	Code
LLM vs Small Model? Large Language Model Based Text Augmentation Enhanced Personality Detection Model	arXiv	2024-03
Evolving Knowledge Distillation with Large Language Models and Active Learning	arXiv	2024-03
Mixed Distillation Helps Smaller Language Model Better Reasoning	arXiv	2023-12
PromptMix: A Class Boundary Augmentation Method for Large Language Model Distillation	EMNLP	2023-10	Github
TinyLLM: Learning a Small Student from Multiple Large Language Models	arXiv	2024-02
Targeted Data Generation: Finding and Fixing Model Weaknesses	ACL	2023-05	Github
Distilling ChatGPT for Explainable Automated Student Answer Assessment	arXiv	2023-05	Github
ChatGPT outperforms crowd workers for text-annotation tasks	arXiv	2023-03
Annollm: Making large language models to be better crowdsourced annotators	arXiv	2023-03
AugGPT: Leveraging ChatGPT for Text Data Augmentation	arXiv	2023-02	Github
Is GPT-3 a Good Data Annotator?	ACL	2022-12	Github
SunGen: Self-Guided Noise-Free Data Generation for Efficient Zero-Shot Learning	ICLR	2022-05	Github
ZeroGen: Efficient Zero-shot Learning via Dataset Generation	EMNLP	2022-02	Github
Generating Training Data with Language Models: Towards Zero-Shot Language Understanding	NeurIPS	2022-02	Github
Towards Zero-Label Language Learning	arXiv	2021-09
Generate, Annotate, and Learn: NLP with Synthetic Text	TACL	2021-06

NLG

Title	Venue	Date	Code	Data
Tailoring Self-Rationalizers with Multi-Reward Distillation	arXiv	2023-11	Github	Data
RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation	arXiv	2023-10	Github
Neural Machine Translation Data Generation and Augmentation using ChatGPT	arXiv	2023-07
On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes	ICLR	2023-06
Can LLMs generate high-quality synthetic note-oriented doctor-patient conversations?	arXiv	2023-06	Github	Data
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT	EMNLP	2023-05
Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing	arXiv	2023-05	Github
Data Augmentation for Radiology Report Simplification	Findings of EACL	2023-04	Github
Want To Reduce Labeling Cost? GPT-3 Can Help	Findings of EMNLP	2021-08

Information Retrieval

Title	Venue	Date	Code	Data
InstructDistill: Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers	arXiv	2023-11	Github	Data
Soft prompt tuning for augmenting dense retrieval with large language models	arXiv	2023-07	Github
Query Rewriting in Retrieval-Augmented Large Language Models	EMNLP	2023-05
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents	EMNLP	2023-04	Github	Data
AugTriever: Unsupervised Dense Retrieval by Scalable Data Augmentation	arXiv	2022-12	Github
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation	EMNLP	2022-10
Promptagator: Few-shot Dense Retrieval From 8 Examples	ICLR	2022-09
Questions Are All You Need to Train a Dense Passage Retrieval	TACL	2022-06	Github
Improving Passage Retrieval with Zero-Shot Question Generation	EMNLP	2022-04	Github	Data
InPars: Data Augmentation for Information Retrieval using Large Language Models	arXiv	2022-02	Github	Data
Generating Datasets with Pretrained Language Models	EMNLP	2021-04	Github

Recommendation

Title	Venue	Date	Code	Data
Can Small Language Models be Good Reasoners for Sequential Recommendation?	arXiv	2024-03
Large Language Model Augmented Narrative Driven Recommendations	arXiv	2023-06
Recommendation as Instruction Following: A Large Language Model Empowered Recommendation Approach	arXiv	2023-05
ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models	WSDM	2023-05	Github	Data

Text Generation Evaluation

Title	Venue	Date	Code	Data
Prometheus: Inducing Fine-grained Evaluation Capability in Language Models	ICLR	2023-10	Github	Data
TIGERScore: Towards Building Explainable Metric for All Text Generation Tasks	arXiv	2023-10	Github	Data
Generative Judge for Evaluating Alignment	ICLR	2023-10	Github	Data
PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization	arXiv	2023-06	Github	Data
INSTRUCTSCORE: Explainable Text Generation Evaluation with Fine-grained Feedback	EMNLP	2023-05	Github	Data

Code

Title	Venue	Date	Code	Data
Magicoder: Source Code Is All You Need	arXiv	2023-12	Github	Data <br> Data
WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation	arXiv	2023-12
Instruction Fusion: Advancing Prompt Evolution through Hybridization	arXiv	2023-12
MFTCoder: Boosting Code LLMs with Multitask Fine-Tuning	arXiv	2023-11	Github	Data <br> Data
LLM-Assisted Code Cleaning For Training Accurate Code Generators	arXiv	2023-11
Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation	EMNLP	2023-10	Github
Code Llama: Open Foundation Models for Code	arXiv	2023-08	Github
Distilled GPT for Source Code Summarization	arXiv	2023-08	Github	Data
Textbooks Are All You Need: A Large-Scale Instructional Text Data Set for Language Models	arXiv	2023-06
Code Alpaca: An Instruction-following LLaMA model for code generation	-	2023-03	Github	Data

Multi-Modality

Title	Venue	Date	Code	Data
Miko: Multimodal Intention Knowledge Distillation from Large Language Models for Social-Media Commonsense Discovery	arXiv	2024-02
Localizing Visual Commonsense Knowledge in Large Language Models	NeurIPS	2023-12	Github	Data
To See is to Believe: Prompting GPT-4V for Better Visual Instruction Tuning	arXiv	2023-11	Github	Data
ILuvUI: Instruction-tuned LangUage-Vision modeling of UIs from Machine Conversations	arXiv	2023-10
NExT-GPT: Any-to-Any Multimodal LLM	arXiv	2023-09	Github	Data
StableLLaVA: Enhanced Visual Instruction Tuning with Synthesized Image-Dialogue Data	arXiv	2023-08	Github	Data
PointLLM: Empowering Large Language Models to Understand Point Clouds	arXiv	2023-08	Github	Data
SVIT: Scaling up Visual Instruction Tuning	arXiv	2023-07	Github	Data
ChatSpot: Bootstrapping Multimodal LLMs via Precise Referring Instruction Tuning	arXiv	2023-07
Shikra: Unleashing Multimodal LLM's Referential Dialogue Magic	arXiv	2023-06	Github	Data
Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning	ICLR	2023-06	Github	Data
Valley: Video Assistant with Large Language model Enhanced abilitY	arXiv	2023-06	Github	Data
DetGPT: Detect What You Need via Reasoning	EMNLP	2023-05	Github
Visual Instruction Tuning: A Comprehensive Study of Visual Instruction Tuning for Large Language Models	NeurIPS	2023-04	Github	Data

Summary Table

<div align="center"> <img src="imgs/table.jpg"><br> <em>Figure: A summary of representative works about skill distillation.</em> </div> <br>

Verticalization Distillation

Law

Title	Venue	Date	Code	Data
Fuzi	-	2023-08	Github
ChatLaw: Open-Source Legal Large Language Model with Integrated External Knowledge Bases	arXiv	2023-06	Github
Lawyer LLaMA Technical Report	arXiv	2023-05	Github	Data

Medical & Healthcare

Title	Venue	Date	Code	Data
HuatuoGPT-II, One-stage Training for Medical Adaption of LLMs	arXiv	2023-11	Github	Data
AlpaCare: Instruction-tuned large language models for medical application	arXiv	2023-10	Github	Data
DISC-MedLLM: Bridging General Large Language Models and Real-World Medical Consultation	arXiv	2023-08	Github	Data
HuatuoGPT: Taming Language Model to Be a Doctor	EMNLP	2023-05	Github	Data
DoctorGLM: Fine-tuning your Chinese doctor is not a herculean task	arXiv	2023-04	Github	Data
Huatuo: Tuning LLM with Chinese Medical Knowledge	arXiv	2023-04	Github
MedAlpaca: An Open-Source Collection of Medical Conversational AI Models and Training Data	arXiv	2023-04	Github	Data
PMC-LLaMA: Further Finetuning LLaMA on Medical Papers	arXiv	2023-04	Github	Data
ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledge	arXiv	2023-03	Github

Finance

Title	Venue	Date	Code	Data
XuanYuan 2.0: A Large Chinese Financial Chat Model with Hundreds of Billions Parameters	CIKM	2023-05

Science

Title	Venue	Date	Code	Data
MuseGraph: Graph-oriented Instruction Tuning of Large Language Models for Generic Graph Mining	arXiv	2024-03
SciGLM: Training Scientific Language Models with Self-Reflective Instruction Annotation and Tuning	arXiv	2024-01	Github
AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets	arXiv	2024-01
GeoGalactica: A Scientific Large Language Model in Geoscience	arXiv	2024-01	Github	Data
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery	arXiv	2023-11	Github
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions	arXiv	2023-10	Github
OceanGPT: A Large Language Model for Ocean Science Tasks	arXiv	2023-10	Github	Data
MarineGPT: Unlocking Secrets of Ocean to the Public	arXiv	2023-10	Github
Mammoth: Building Math Generalist Models through Hybrid Instruction Tuning	arXiv	2023-09	Github	Data
ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solving	ICLR	2023-09	Github
DARWIN Series: Domain Specific Large Language Models for Natural Science	arXiv	2023-08	Github
Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct	arXiv	2023-08	Github
Biomedgpt: Open Multimodal Generative Pre-trained Transformer for Biomedicine	arXiv	2023-08	Github	Data
Prot2Text: Multimodal Protein’s Function Generation with GNNs and Transformers	NeurIPS	2023-07
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein	bioRxiv	2023-07
GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learning	NeurIPS	2023-06	Github	Data
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization	arXiv	2023-06	Github
Visual Instruction Tuning: A Comprehensive Study of Visual Instruction Tuning for Large Language Models	NeurIPS	2023-04	Github	Data

Misc.

Title	Venue	Date	Code	Data
OWL: A Large Language Model for IT Operations	arXiv	2023-09	Github	Data
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education	arXiv	2023-08	Github	Data

Encoder-based KD

Note: Our survey mainly focuses on generative LLMs, and thus the encoder-based KD is not included in the survey. However, we are also interested in this topic and continue to update the latest works in this area.

Title	Venue	Date	Code	Data
Masked Latent Semantic Modeling: an Efficient Pre-training Alternative to Masked Language Modeling	Findings of ACL	2023-08
Better Together: Jointly Using Masked Latent Semantic Modeling and Masked Language Modeling for Sample Efficient Pre-training	CoNLL	2023-08

Citation

If you find this repository helpful, please consider citing the following paper:

@misc{xu2024survey,
      title={A Survey on Knowledge Distillation of Large Language Models}, 
      author={Xiaohan Xu and Ming Li and Chongyang Tao and Tao Shen and Reynold Cheng and Jinyang Li and Can Xu and Dacheng Tao and Tianyi Zhou},
      year={2024},
      eprint={2402.13116},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}