Gespeichert in:
| Hauptverfasser: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2404.15676 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866917914201817088 |
|---|---|
| author | Xia, Yu Wang, Rui Liu, Xu Li, Mingyan Yu, Tong Chen, Xiang McAuley, Julian Li, Shuai |
| author_facet | Xia, Yu Wang, Rui Liu, Xu Li, Mingyan Yu, Tong Chen, Xiang McAuley, Julian Li, Shuai |
| contents | Chain-of-Thought (CoT) has been a widely adopted prompting method, eliciting impressive reasoning abilities of Large Language Models (LLMs). Inspired by the sequential thought structure of CoT, a number of Chain-of-X (CoX) methods have been developed to address various challenges across diverse domains and tasks involving LLMs. In this paper, we provide a comprehensive survey of Chain-of-X methods for LLMs in different contexts. Specifically, we categorize them by taxonomies of nodes, i.e., the X in CoX, and application tasks. We also discuss the findings and implications of existing CoX methods, as well as potential future directions. Our survey aims to serve as a detailed and up-to-date resource for researchers seeking to apply the idea of CoT to broader scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15676 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs Xia, Yu Wang, Rui Liu, Xu Li, Mingyan Yu, Tong Chen, Xiang McAuley, Julian Li, Shuai Computation and Language Artificial Intelligence Chain-of-Thought (CoT) has been a widely adopted prompting method, eliciting impressive reasoning abilities of Large Language Models (LLMs). Inspired by the sequential thought structure of CoT, a number of Chain-of-X (CoX) methods have been developed to address various challenges across diverse domains and tasks involving LLMs. In this paper, we provide a comprehensive survey of Chain-of-X methods for LLMs in different contexts. Specifically, we categorize them by taxonomies of nodes, i.e., the X in CoX, and application tasks. We also discuss the findings and implications of existing CoX methods, as well as potential future directions. Our survey aims to serve as a detailed and up-to-date resource for researchers seeking to apply the idea of CoT to broader scenarios. |
| title | Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2404.15676 |