Can we Evaluate RAGs with Synthetic Data?
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909859897671680 |
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| author | van Elburg, Jonas van der Putten, Peter Marx, Maarten |
| author_facet | van Elburg, Jonas van der Putten, Peter Marx, Maarten |
| contents | We investigate whether synthetic question-answer (QA) data generated by large language models (LLMs) can serve as an effective proxy for human-labeled benchmarks when the latter is unavailable. We assess the reliability of synthetic benchmarks across two experiments: one varying retriever parameters while keeping the generator fixed, and another varying the generator with fixed retriever parameters. Across four datasets, of which two open-domain and two proprietary, we find that synthetic benchmarks reliably rank the RAGs varying in terms of retriever configuration, aligning well with human-labeled benchmark baselines. However, they do not consistently produce reliable RAG rankings when comparing generator architectures. The breakdown possibly arises from a combination of task mismatch between the synthetic and human benchmarks, and stylistic bias favoring certain generators. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2508_11758 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Can we Evaluate RAGs with Synthetic Data? van Elburg, Jonas van der Putten, Peter Marx, Maarten Computation and Language Artificial Intelligence We investigate whether synthetic question-answer (QA) data generated by large language models (LLMs) can serve as an effective proxy for human-labeled benchmarks when the latter is unavailable. We assess the reliability of synthetic benchmarks across two experiments: one varying retriever parameters while keeping the generator fixed, and another varying the generator with fixed retriever parameters. Across four datasets, of which two open-domain and two proprietary, we find that synthetic benchmarks reliably rank the RAGs varying in terms of retriever configuration, aligning well with human-labeled benchmark baselines. However, they do not consistently produce reliable RAG rankings when comparing generator architectures. The breakdown possibly arises from a combination of task mismatch between the synthetic and human benchmarks, and stylistic bias favoring certain generators. |
| title | Can we Evaluate RAGs with Synthetic Data? |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2508.11758 |