Summary of Beyond Chain-of-thought: a Survey Of Chain-of-x Paradigms For Llms, by Yu Xia et al.
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
by Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, Shuai Li
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper provides a comprehensive survey of Chain-of-X (CoX) methods for Large Language Models (LLMs), building on the popular Chain-of-Thought (CoT) prompting method. The authors categorize CoX methods by taxonomies of nodes, or “X,” and application tasks. They discuss existing findings and implications, as well as potential future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at different ways to use a technique called Chain-of-X with Large Language Models. It’s like building on a popular idea called Chain-of-Thought, which helps these models think in a more human-like way. The authors group these methods by what they’re working with and what task they’re trying to accomplish. They also talk about what we’ve learned so far and where this research might go next. |
Keywords
» Artificial intelligence » Prompting
