Summary of Refutebench: Evaluating Refuting Instruction-following For Large Language Models, by Jianhao Yan et al.
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
by Jianhao Yan, Yun Luo, Yue Zhang
First submitted to arxiv on: 21 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 The proposed RefuteBench benchmark evaluates large language models’ ability to respond to users’ feedback, analyzing whether they can adapt to refuting instructions and consistently follow user demands. The study finds that most LLMs are “stubborn” and tend to rely on their internal knowledge, often ignoring user feedback. As conversations grow longer, the models increasingly forget user feedback, reverting to their default responses. To improve responsiveness, the authors suggest recall-and-repeat prompts as a simple yet effective solution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are getting better at understanding what we say, but they still have trouble listening to our instructions. Imagine you’re having a conversation with a chatbot, and it keeps giving you wrong answers. That’s kind of what’s happening here. Researchers created a special test to see how well these AI models can take feedback and adjust their answers accordingly. They found that most models are pretty stubborn and don’t really listen to us. As the conversation goes on, they tend to forget what we’re saying and go back to giving us wrong answers. The solution is simple: just ask them to recall what you said earlier and repeat it back to you. That helps them stay on track! |
Keywords
» Artificial intelligence » Recall
