Summary of Sample-efficient Human Evaluation Of Large Language Models Via Maximum Discrepancy Competition, by Kehua Feng et al.
Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
by Kehua Feng, Keyan Ding, Kede Ma, Zhihua Wang, Qiang Zhang, Huajun Chen
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Human-Computer Interaction (cs.HC)
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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 proposes an innovative approach to evaluating large language models (LLMs) in an automated and unbiased manner. The method, called Maximum Discrepancy (MAD), uses a competition-based framework that selects a small set of informative and diverse instructions for two LLMs to respond to. Human subjects then evaluate the responses through a three-alternative forced choice task. The results are aggregated using the Elo rating system, providing a reliable ranking of the LLMs’ capabilities across four skills: knowledge understanding, mathematical reasoning, writing, and coding. The proposed method is sample-efficient and can be used for large-scale evaluations, addressing the limitations of standard metrics and human evaluation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us figure out which language models are really good at certain tasks. Right now, it’s hard to compare these models because we have to test them one by one, and that takes a lot of time and money. To solve this problem, the researchers created a new way to evaluate language models quickly and fairly. They show people two different responses from each model, and ask which one is better. Then, they combine all those answers into a single ranking that shows which model is best at what task. This new method helps us understand what makes some language models better than others, and how we can make them even better. |
