Reward Models in Focus: RAG-RewardBench – A New Benchmark for Retrieval Augmented Generation

Retrieval Augmented Generation (RAG) has proven to be a promising approach for improving the credibility and traceability of Large Language Models (LLMs). By incorporating external information sources, RAG systems can generate more accurate and well-founded answers. However, a frequently neglected aspect is the optimal alignment of these models with human preferences. Reward Models (RMs) play a crucial role here, as they act as proxies for human values and guide the optimization process. But how does one evaluate and select a reliable RM for preference alignment in RAG systems? RAG-RewardBench, a new benchmark, addresses precisely this challenge.

RAG-Specific Scenarios and Datasets

RAG-RewardBench was developed to comprehensively evaluate RMs in RAG contexts. The benchmark encompasses four central scenarios that reflect the specific challenges of RAG systems:

- Multi-Hop Reasoning: The ability to derive conclusions from multiple information sources. - Fine-Grained Citation: The precise specification of the sources used. - Appropriate Abstention: The ability to recognize when insufficient information is available for an answer. - Conflict Robustness: Handling conflicting information from different sources.

To ensure the diversity of data sources, RAG-RewardBench integrates 18 RAG subsets, six different retrievers, and 24 RALMs. This broad base allows for a differentiated assessment of the performance of RMs.

LLMs as Evaluators: Efficient and Effective Annotation

An innovative aspect of RAG-RewardBench is the use of LLMs as evaluators for preference annotation. This approach increases efficiency and significantly reduces manual effort. Studies show a strong correlation between the evaluations of LLMs and human annotations, which underlines the validity of this procedure.

Evaluation of Existing RMs and RALMs

With RAG-RewardBench, a comprehensive evaluation of 45 RMs was conducted. The results reveal limitations of existing RMs, particularly in the RAG-specific scenarios. Furthermore, the analysis shows that pre-trained RALMs exhibit little improvement in preference alignment. This highlights the need to focus future research on preference-based training.

Outlook and Significance for the Future of RAG

RAG-RewardBench represents an important step in the development and optimization of RAG systems. The benchmark offers a standardized platform for the evaluation of RMs and thus enables the development of models that are better aligned with human needs. The insights from the evaluations can contribute to further improving the credibility, transparency, and usefulness of RAG systems and fully exploiting the potential of this technology.

The benchmark and the associated code are publicly available and are intended to drive future research in this area.

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