Summary of Beyond Instruction Following: Evaluating Inferential Rule Following Of Large Language Models, by Wangtao Sun et al.
Beyond Instruction Following: Evaluating Inferential Rule Following of Large Language Models
by Wangtao Sun, Chenxiang Zhang, XueYou Zhang, Xuanqing Yu, Ziyang Huang, Pei Chen, Haotian Xu, Shizhu He, Jun Zhao, Kang Liu
First submitted to arxiv on: 11 Jul 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 The paper investigates the inferential rule-following capability of Large Language Models (LLMs), which is crucial for their safe, accurate, and intelligent use in real-world scenarios. Existing studies have failed to distinguish between inferential rule-following and instruction-following scenarios, making it unclear how well LLMs can follow rules. To address this gap, the authors propose a comprehensive benchmark called RuleBench and evaluate various LLMs on their ability to follow rules. The results show that current LLMs are still limited in their rule-following capabilities, but the authors also introduce Inferential Rule-Following Tuning (IRFT), which enables LLMs to learn abstract rule-following abilities from synthetic data and generalize to RuleBench. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very good at understanding and generating text. However, they need to be able to follow rules and make decisions based on those rules in order to be truly useful. Right now, it’s not clear how well LLMs can do this. This paper tries to fix that by creating a special test to see if LLMs can really follow rules. The results show that current LLMs are still not very good at following rules. But the authors also have some ideas for how to make LLMs better at this. |
Keywords
» Artificial intelligence » Synthetic data
