Summary of Rag-rewardbench: Benchmarking Reward Models in Retrieval Augmented Generation For Preference Alignment, by Zhuoran Jin et al.
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment
by Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao
First submitted to arxiv on: 18 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 The proposed RAG-RewardBench benchmark evaluates reward models (RMs) in retrieval augmented language models (RALMs) to align with human preferences. The benchmark assesses RM performance in four challenging scenarios: multi-hop reasoning, fine-grained citation, appropriate abstain, and conflict robustness. A diverse dataset comprising 18 subsets, six retrievers, and 24 RALMs is used to increase the variety of data sources. An LLM-as-a-judge approach improves preference annotation efficiency and effectiveness, showing a strong correlation with human annotations. The evaluation of 45 RMs reveals their limitations in RAG scenarios, highlighting the need for preference-aligned model releases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new benchmark to evaluate reward models in retrieval augmented language models. It helps us understand how well these models can align with what humans prefer. To do this, it designs four challenging tests and uses many different datasets and models. The results show that some reward models are better than others at understanding human preferences. |
Keywords
» Artificial intelligence » Rag
