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The rise of large language models (LLMs) as evaluators of their own outputs is gaining attention in the tech community. Hailey Schoelkopf from EleutherAI highlighted the potential biases that LLMs might introduce, such as favoring longer or more authoritative answers. The concept of LLM-as-a-judge involves using these models to assess the quality of responses generated by other LLMs. However, experts caution that this method can be unreliable, particularly when LLMs are asked to produce numeric scores, as a 1-10 rating is imprecise. Human evaluation remains critical in the development of LLMs. Alternatives and best practices for using LLM evaluators are being explored, with resources like the OpenAI cookbook providing guidance on strategies for detecting hallucinations in LLM responses.
LLM-as-a-judge is a very powerful technique but it's difficult to get reliable results. The #1 mistake I see people make is to have an LLM produce a numeric score, which, like asking a human to rate 1-10, is not precise. We reproduce that and walk through how to do better :) https://t.co/bsx96xylt8
LLM-as-a-judge scorers are a powerful tool you can use when you need to evaluate more complex responses to LLM calls. We published an OpenAI cookbook to work through different strategies for detecting hallucinations — check it out in the @OpenAIDevs cookbook library. https://t.co/25FJfadcOe
👩⚖️ How to use LLM-as-a-judge to evaluate LLM systems? We put together an in-depth practical guide: ✍️ What is LLM-as-a-judge 🏗 How to build an LLM judge ✅ What makes a good prompt ⚖️ Alternatives to LLM evaluators https://t.co/nXni5rUA61 https://t.co/B6mU1ijuxB
