Summary of Conqret: Benchmarking Fine-grained Evaluation Of Retrieval Augmented Argumentation with Llm Judges, by Kaustubh D. Dhole et al.
ConQRet: Benchmarking Fine-Grained Evaluation of Retrieval Augmented Argumentation with LLM Judges
by Kaustubh D. Dhole, Kai Shu, Eugene Agichtein
First submitted to arxiv on: 6 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 explores the development of Retrieval-Augmented Argumentation (RAArg) for generating nuanced, evidence-based answers on controversial topics. The authors leverage LLM capabilities to provide high-quality arguments grounded in real-world evidence. However, evaluating RAArg is challenging due to the complexity and length of answers on debated topics. To address this gap, the researchers propose automated evaluation methods using multiple fine-grained LLM judges, which provide better assessments than traditional single-score metrics or human crowdsourcing. The authors introduce ConQRet, a new benchmark featuring long and complex human-authored arguments on debated topics, grounded in real-world websites. They validate their proposed techniques on the prior dataset and the new ConQRet benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at answering tricky questions. It’s trying to find ways for computers to give good answers that are based on real evidence, not just made-up opinions. This is important because people often argue about things like abortion or vaccination without looking at the facts. The authors think they can do this by using special language models and testing them with a new kind of dataset that has long and complicated arguments. They want to see if their way works better than other ways of evaluating computer-generated answers. |
