Summary of Mixeval: Deriving Wisdom Of the Crowd From Llm Benchmark Mixtures, by Jinjie Ni et al.
MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures
by Jinjie Ni, Fuzhao Xue, Xiang Yue, Yuntian Deng, Mahir Shah, Kabir Jain, Graham Neubig, Yang You
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 proposes a new approach to evaluating large language models (LLMs) called MixEval, which combines comprehensive real-world user queries with efficient ground-truth-based benchmarks. The authors highlight the limitations of traditional benchmarks, including grading biases and limited query quantity, and argue that existing evaluation methods, such as Chatbot Arena, are costly and slow. The proposed paradigm bridges the gap between these two approaches by matching web-mined user queries with similar queries from existing benchmarks. The authors also introduce MixEval-Hard, which offers more room for model improvement. The key advantages of this approach include a high correlation (0.96) with Chatbot Arena, fast and cheap execution, and dynamic evaluation enabled by rapid data updates. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are being evaluated in a way that’s not very accurate or efficient. Researchers have tried to use real-world questions and answers, but these methods can be biased and don’t capture the complexity of how people actually ask questions. Some other methods try to grade the models themselves, but this is also limited because it only looks at a small number of questions. This new approach combines the best of both worlds by using a mix of real-world questions and existing benchmarks. It’s faster, cheaper, and more accurate than current methods, making it an important step forward in understanding how to evaluate large language models. |
