Summary of Trust or Escalate: Llm Judges with Provable Guarantees For Human Agreement, by Jaehun Jung et al.
Trust or Escalate: LLM Judges with Provable Guarantees for Human Agreement
by Jaehun Jung, Faeze Brahman, Yejin Choi
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This research proposes a rigorous approach to evaluating language models using large language models (LLMs) with a guarantee of human agreement. The method assesses the confidence of LLMs and selectively decides when to trust their judgment, ensuring that model evaluations align with those of humans to a user-specified level. To achieve this, the authors introduce Simulated Annotators, a novel confidence estimation method that improves judge calibration and enables high coverage of evaluated instances. The framework also includes Cascaded Selective Evaluation, which uses cheaper models as initial judges and escalates to stronger models only when necessary. Experimental results show that this approach guarantees strong alignment with humans, even using cost-effective models like Mistral-7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us evaluate language models in a better way. It shows how we can use large language models to make sure our evaluations agree with what humans think is correct. The method looks at the confidence of these big models and only uses their judgment when it’s right. To make this happen, the authors created a new way to measure confidence called Simulated Annotators. They also came up with a plan called Cascaded Selective Evaluation that uses smaller models first and then goes to stronger ones only if needed. The results show that this method works really well and can even use cheaper models like Mistral-7B. |
Keywords
» Artificial intelligence » Alignment
