Summary of Wikicontradict: a Benchmark For Evaluating Llms on Real-world Knowledge Conflicts From Wikipedia, by Yufang Hou et al.
WikiContradict: A Benchmark for Evaluating LLMs on Real-World Knowledge Conflicts from Wikipedia
by Yufang Hou, Alessandra Pascale, Javier Carnerero-Cano, Tigran Tchrakian, Radu Marinescu, Elizabeth Daly, Inkit Padhi, Prasanna Sattigeri
First submitted to arxiv on: 19 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 introduces a comprehensive evaluation of large language models (LLMs) when retrieving passages from Wikipedia, which contains contradictory information. The authors create WikiContradict, a benchmark consisting of 253 high-quality instances, to assess LLM performance under different scenarios. They evaluate various LLMs using both human evaluations and an automated model, achieving an F-score of 0.8. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are powerful tools for generating answers to questions. But what happens when these answers come from contradictory passages? This paper looks at how well different LLMs handle this situation by creating a benchmark called WikiContradict. It’s like testing how well you can find the right answer in a library with multiple books that say different things! |
