Summary of Calibrating Reasoning in Language Models with Internal Consistency, by Zhihui Xie et al.
Calibrating Reasoning in Language Models with Internal Consistency
by Zhihui Xie, Jizhou Guo, Tong Yu, Shuai Li
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 investigates the internal representations of large language models (LLMs) and their ability to robustly process generated rationales. The authors find that while generated rationales improve answer accuracy, inconsistencies emerge between the model’s internal representations in middle layers and those in final layers, potentially undermining the reliability of their reasoning processes. To address this, the authors propose a new approach to calibrate reasoning by up-weighting reasoning paths with high internal consistency, resulting in a significant boost in reasoning performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how large language models work inside when they’re trying to figure things out. It turns out that these models can be really good at answering questions, but sometimes their answers are wrong or don’t make sense. The researchers want to know why this happens and how it affects the model’s ability to think critically. They found that the model’s internal thoughts get mixed up when it tries to explain its answers, which makes it less reliable. To fix this, they came up with a new way to help the model be more honest about what it knows and doesn’t know. |
