On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918153407168512 |
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| author | Ding, Hanxing Tao, Shuchang Pang, Liang Wei, Zihao Chen, Liwei Xu, Kun Shen, Huawei Cheng, Xueqi |
| author_facet | Ding, Hanxing Tao, Shuchang Pang, Liang Wei, Zihao Chen, Liwei Xu, Kun Shen, Huawei Cheng, Xueqi |
| contents | Retrieval-augmented generation (RAG) systems traditionally employ sophisticated training strategies to enhance robustness against retrieval noise. In this work, we investigate a critical question: does the benefit of these complex robust training methods diminish as language models become more powerful? Through systematic evaluation across multiple model scales and question-answering datasets, our analysis reveals a consistent trend: \emph{the marginal robustness benefit of sophisticated training strategies decreases substantially as model capacity increases.} While smaller models show significant performance improvements from complex document selection and adversarial objectives, more capable models achieve comparable or even superior performance with simpler training approaches. Further investigation demonstrates that stronger models naturally exhibit better confidence calibration, cross-dataset generalization capability, and more effective attention patterns, even under simple training regimes. These findings suggest that as foundation models evolve, the engineering effort invested in complex robust training may yield diminishing returns, indicating that simplified RAG pipelines could suffice for powerful models while maintaining competitive performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_11400 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs Ding, Hanxing Tao, Shuchang Pang, Liang Wei, Zihao Chen, Liwei Xu, Kun Shen, Huawei Cheng, Xueqi Computation and Language Retrieval-augmented generation (RAG) systems traditionally employ sophisticated training strategies to enhance robustness against retrieval noise. In this work, we investigate a critical question: does the benefit of these complex robust training methods diminish as language models become more powerful? Through systematic evaluation across multiple model scales and question-answering datasets, our analysis reveals a consistent trend: \emph{the marginal robustness benefit of sophisticated training strategies decreases substantially as model capacity increases.} While smaller models show significant performance improvements from complex document selection and adversarial objectives, more capable models achieve comparable or even superior performance with simpler training approaches. Further investigation demonstrates that stronger models naturally exhibit better confidence calibration, cross-dataset generalization capability, and more effective attention patterns, even under simple training regimes. These findings suggest that as foundation models evolve, the engineering effort invested in complex robust training may yield diminishing returns, indicating that simplified RAG pipelines could suffice for powerful models while maintaining competitive performance. |
| title | On the Diminishing Returns of Complex Robust RAG Training in the Era of Powerful LLMs |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.11400 |