Summary of Nash Cot: Multi-path Inference with Preference Equilibrium, by Ziqi Zhang et al.
Nash CoT: Multi-Path Inference with Preference Equilibrium
by Ziqi Zhang, Cunxiang Wang, Xiong Xiao, Yue Zhang, Donglin Wang
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new reasoning framework called Chain of Thought (CoT) to improve the performance of Large Language Models (LLMs) on complex inference tasks. The authors highlight the effectiveness of multi-path inference, but note that increasing the number of inference paths can lead to increased computational costs. To address this limitation, they suggest using question-related role templates to guide LLMs into relevant roles, which reduces dependence on the number of inference paths while improving reasoning accuracy. However, this approach may reduce LLM diversity and performance on tasks where role dependence is low. The authors propose Nash CoT, a game-based system that balances role-specific and general LLM generations, ensuring effective role adoption and diversity in LLM generation. They evaluate Nash CoT across various inference tasks, achieving comparable or better results than multi-path CoT with the same number of inference paths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using a new way to help Large Language Models (LLMs) make better decisions. It’s called Chain of Thought, and it makes LLMs work better on tricky problems. One problem is that when LLMs have to make many decisions, they can get stuck in one way of thinking. To fix this, the authors suggest giving LLMs a “role” to play based on the question being asked. This helps LLMs make more accurate predictions and reduces the need for them to make as many decisions. However, this approach has some limitations, such as reducing diversity in the LLM’s thoughts. The authors propose a new way called Nash CoT that balances giving LLMs roles with keeping their thinking diverse. They tested this approach on different tasks and found it worked just as well or better than other methods. |
Keywords
* Artificial intelligence * Inference
