Summary of Varying Shades Of Wrong: Aligning Llms with Wrong Answers Only, by Jihan Yao et al.
Varying Shades of Wrong: Aligning LLMs with Wrong Answers Only
by Jihan Yao, Wenxuan Ding, Shangbin Feng, Lucy Lu Wang, Yulia Tsvetkov
First submitted to arxiv on: 14 Oct 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 explores ways to improve large language models’ (LLMs) capabilities in the absence of reliable annotations. The researchers investigate two questions: can LLMs generate reliable preferences among incorrect options, and would aligning with such preferences be helpful? To address this, they employ methods like self-consistency, token probabilities, and LLM-as-a-judge to elicit wrong-over-wrong preferences. They then fine-tune language models using preference optimization approaches based on these synthesized preferences. The paper presents extensive experiments with seven LLMs and eight datasets, demonstrating that LLMs can distinguish between various shades of incorrect answers (up to 20.9% better than random guess) and that aligning with wrong-over-wrong preferences improves model calibration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps make language models better at understanding when something is wrong. The scientists want to know if these models can tell the difference between different levels of incorrect answers. They use special techniques to get the models to choose which of two wrong answers is worse. Then, they fine-tune the models using this information. The study shows that language models can indeed make some correct choices (up to 21% better than just guessing) and that using this technique improves how well the models understand when something is incorrect. |
Keywords
» Artificial intelligence » Optimization » Token
