Summary of Pedants: Cheap but Effective and Interpretable Answer Equivalence, by Zongxia Li et al.
PEDANTS: Cheap but Effective and Interpretable Answer Equivalence
by Zongxia Li, Ishani Mondal, Yijun Liang, Huy Nghiem, Jordan Lee Boyd-Graber
First submitted to arxiv on: 17 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 paper addresses the limitations of current answer correctness (AC) metrics in question answering (QA), which struggle to evaluate large language models’ (LLMs’) free-form answers. The authors highlight two challenges: a lack of diverse evaluation data and over-reliance on expensive LLMs. To overcome these issues, they provide rubrics and datasets for evaluating machine QA inspired by the Trivia community. Additionally, they propose an efficient and interpretable QA evaluation method that outperforms exact match and neural approaches (BERTScore). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in question answering where current answer correctness metrics don’t work well with long answers from big language models. There are two main issues: not enough different ways to test these answers, and relying too much on expensive computer programs. To fix this, the authors create guidelines and datasets for testing machine QA based on trivia games. They also suggest a new way to evaluate QA that is fast, easy to understand, and better than existing methods. |
Keywords
» Artificial intelligence » Question answering
