Summary of Weak-to-strong Generalization Beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning, by Ruimeng Ye et al.
Weak-to-Strong Generalization beyond Accuracy: a Pilot Study in Safety, Toxicity, and Legal Reasoning
by Ruimeng Ye, Yang Xiao, Bo Hui
First submitted to arxiv on: 16 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses the critical issue of ensuring large language models (LLMs) align with human values. Traditional methods rely heavily on human feedback, which becomes increasingly challenging as superhuman models emerge. Recent works use weak supervisors to elicit knowledge from stronger models, but this setup is analogous to binary classification rather than practical alignment tasks like safety and toxicity evaluation. This paper bridges the gap by extending weak-to-strong generation to these context-specific alignment tasks. We demonstrate the phenomenon of weak-to-strong generation in three complex alignment tasks: safety, toxicity, and legal reasoning. Additionally, we explore efficient strategies for improving alignment performance to enhance model outcomes. Our findings aim to catalyze research progress on weak-to-strong generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making sure big language models match human values. Right now, these models are getting really smart and can do things that humans can’t understand. To make sure they’re good, we need a way to evaluate them. Some people think using weak supervisors could be the answer, but it’s not easy because these superhuman models are hard to understand. This paper shows how to use weak-to-strong generation to match human values in tasks like safety and toxicity evaluation. We want to make sure our findings help improve language models so they can be useful for us. |
Keywords
» Artificial intelligence » Alignment » Classification » Generalization
