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Awesome-Reasoning-Foundation-Models

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overview

survey.pdf | A curated list of awesome large AI models, or foundation models, for reasoning.

We organize the current foundation models into three categories: language foundation models, vision foundation models, and multimodal foundation models. Further, we elaborate the foundation models in reasoning tasks, including commonsense, mathematical, logical, causal, visual, audio, multimodal, agent reasoning, etc. Reasoning techniques, including pre-training, fine-tuning, alignment training, mixture of experts, in-context learning, and autonomous agent, are also summarized.

We welcome contributions to this repository to add more resources. Please submit a pull request if you want to contribute! See CONTRIBUTING.

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Table of Contents

<details open> <summary>table of contents</summary> </details>

0 Survey

overview

This repository is primarily based on the following paper:

A Survey of Reasoning with Foundation Models <br>

Jiankai Sun, Chuanyang Zheng, Enze Xie, Zhengying Liu, Ruihang Chu, Jianing Qiu, Jiaqi Xu, Mingyu Ding, Hongyang Li, Mengzhe Geng, Yue Wu, Wenhai Wang, Junsong Chen, Zhangyue Yin, Xiaozhe Ren, Jie Fu, Junxian He, Wu Yuan, Qi Liu, Xihui Liu, Yu Li, Hao Dong, Yu Cheng, Ming Zhang, Pheng Ann Heng, Jifeng Dai, Ping Luo, Jingdong Wang, Ji-Rong Wen, Xipeng Qiu, Yike Guo, Hui Xiong, Qun Liu, and Zhenguo Li

If you find this repository helpful, please consider citing:

@article{sun2023survey,
  title={A Survey of Reasoning with Foundation Models},
  author={Sun, Jiankai and Zheng, Chuanyang and Xie, Enze and Liu, Zhengying and Chu, Ruihang and Qiu, Jianing and Xu, Jiaqi and Ding, Mingyu and Li, Hongyang and Geng, Mengzhe and others},
  journal={arXiv preprint arXiv:2312.11562},
  year={2023}
}

1 Relevant Surveys and Links

<details open> <summary>relevant surveys</summary>

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</details>

2 Foundation Models

<details open> <summary>foundation models</summary>

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foundation_models

Table of Contents - 2

<details open> <summary>foundation models (table of contents)</summary>

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</details>

2.1 Language Foundation Models

<details open> <summary>LFMs</summary>

Foundation Models (Back-to-Top)

</details> <!-- -->

2.2 Vision Foundation Models

<details open> <summary>VFMs</summary>

Foundation Models (Back-to-Top)

</details> <!-- -->

2.3 Multimodal Foundation Models

<details open> <summary>MFMs</summary>

Foundation Models (Back-to-Top)

</details> <!-- -->

2.4 Reasoning Applications

<details open> <summary>reasoning applications</summary>

Foundation Models (Back-to-Top)

</details> </details>

3 Reasoning Tasks

<details open> <summary>reasoning tasks</summary>

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Table of Contents - 3

<details open> <summary>reasoning tasks (table of contents)</summary> </details> <!-- -->

3.1 Commonsense Reasoning

<details open> <summary>commonsense reasoning</summary>

Reasoning Tasks (Back-to-Top)

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3.1.1 Commonsense Question and Answering (QA)

3.1.2 Physical Commonsense Reasoning

3.1.3 Spatial Commonsense Reasoning

3.1.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.2 Mathematical Reasoning

<details open> <summary>mathematical reasoning</summary>

Reasoning Tasks (Back-to-Top)

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3.2.1 Arithmetic Reasoning

Mathematical Reasoning (Back-to-Top)

3.2.2 Geometry Reasoning

Mathematical Reasoning (Back-to-Top)

3.2.3 Theorem Proving

Mathematical Reasoning (Back-to-Top)

3.2.4 Scientific Reasoning

Mathematical Reasoning (Back-to-Top)

3.2.x Benchmarks, Datasets, and Metrics

Mathematical Reasoning (Back-to-Top)

</details> <!-- -->

3.3 Logical Reasoning

<details open> <summary>logical reasoning</summary>

Reasoning Tasks (Back-to-Top)

<br>

3.3.1 Propositional Logic

3.3.2 Predicate Logic

3.3.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.4 Causal Reasoning

<details open> <summary>causal reasoning</summary>

Reasoning Tasks (Back-to-Top)

<br>

3.4.1 Counterfactual Reasoning

3.4.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.5 Visual Reasoning

<details open> <summary>visual reasoning</summary>

Reasoning Tasks (Back-to-Top)

<br>

3.5.1 3D Reasoning

3.5.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.6 Audio Reasoning

<details open> <summary>audio reasoning</summary>

Reasoning Tasks (Back-to-Top)

<br>

3.6.1 Speech

3.6.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.7 Multimodal Reasoning

<details open> <summary>multimodal reasoning</summary>

Reasoning Tasks (Back-to-Top)

<br>

3.7.1 Alignment

3.7.2 Generation

3.7.3 Multimodal Understanding

3.7.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.8 Agent Reasoning

<details open> <summary>agent reasoning</summary>

Reasoning Tasks (Back-to-Top)

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3.8.1 Introspective Reasoning

3.8.2 Extrospective Reasoning

3.8.3 Multi-agent Reasoning

3.8.4 Driving Reasoning

3.8.x Benchmarks, Datasets, and Metrics

</details> <!-- -->

3.9 Other Tasks and Applications

<details open> <summary>other tasks and applications</summary>

Reasoning Tasks (Back-to-Top)

3.9.1 Theory of Mind (ToM)

3.9.2 LLMs for Weather Prediction

3.9.3 Abstract Reasoning

3.9.4 Defeasible Reasoning

3.9.5 Medical Reasoning

3.9.6 Bioinformatics Reasoning

3.9.7 Long-Chain Reasoning

</details> </details>

4 Reasoning Techniques

<details open> <summary>reasoning techniques</summary>

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Table of Contents - 4

<details open> <summary>reasoning techniques (table of contents)</summary> </details> <!-- -->

4.1 Pre-Training

<details open> <summary>pre-training</summary>

Reasoning Techniques (Back-to-Top)

4.1.1 Data

a. Data - Text
b. Data - Image
c. Data - Multimodality

4.1.2 Network Architecture

a. Encoder-Decoder
b. Decoder-Only
c. CLIP Variants
d. Others
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4.2 Fine-Tuning

<details open> <summary>fine-tuning</summary>

Reasoning Techniques (Back-to-Top)

4.2.1 Data

4.2.2 Parameter-Efficient Fine-tuning

a. Adapter Tuning
b. Low-Rank Adaptation
c. Prompt Tuning
d. Partial Parameter Tuning
e. Mixture-of-Modality Adaption
</details> <!-- -->

4.3 Alignment Training

<details open> <summary>alignment training</summary>

Reasoning Techniques (Back-to-Top)

4.3.1 Data

a. Data - Human
b. Data - Synthesis

4.3.2 Training Pipeline

a. Online Human Preference Training
b. Offline Human Preference Training
</details> <!-- -->

4.4 Mixture of Experts (MoE)

<details open> <summary>mixture of experts</summary>

Reasoning Techniques (Back-to-Top)

</details> <!-- -->

4.5 In-Context Learning

<details open> <summary>in-context learning</summary>

Reasoning Techniques (Back-to-Top)

<br>

4.5.1 Demonstration Example Selection

a. Prior-Knowledge Approach
b. Retrieval Approach

4.5.2 Chain-of-Thought

a. Zero-Shot CoT
b. Few-Shot CoT
c. Multiple Paths Aggregation

4.5.3 Multi-Round Prompting

a. Learned Refiners
b. Prompted Refiners
</details> <!-- -->

4.6 Autonomous Agent

<details open> <summary>autonomous agent</summary>

Reasoning Techniques (Back-to-Top)

</details> </details>