Summary of Confidence Matters: Revisiting Intrinsic Self-correction Capabilities Of Large Language Models, by Loka Li et al.
Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models
by Loka Li, Zhenhao Chen, Guangyi Chen, Yixuan Zhang, Yusheng Su, Eric Xing, Kun Zhang
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper investigates the self-correction capabilities of Large Language Models (LLMs), a topic of growing interest following their recent success. The research identifies an important latent factor, “confidence,” which affects the self-correction process and may lead to unreliable conclusions if overlooked. The study finds that LLMs can understand their own response confidence, motivating the development of an “If-or-Else” (IoE) prompting framework to guide them in assessing this confidence. Experiments demonstrate a consistent improvement in accuracy for self-corrected responses using the IoE-based Prompt. This study sheds light on underlying factors and introduces a practical framework for improving self-correction capabilities with confidence. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can correct their own mistakes, but how do they do it? This paper explores what makes them good at correcting themselves. They found that LLMs have an “inner voice” that helps them decide when to trust or question their answers. To help LLMs make better decisions, the researchers created a new way of giving prompts called “If-or-Else” (IoE). It works by asking the model if it’s confident in its answer and adjusting its response based on that confidence. The results show that this method makes the model more accurate when correcting itself. |
Keywords
» Artificial intelligence » Prompt » Prompting
