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Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA

Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur, Daniel Stuart Schiff, Tunazzina Islam · Mar 25, 2026 · Citations: 0

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Mar 25, 2026, 5:54 PM

Stale

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Mar 25, 2026, 5:54 PM

Stale

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Abstract

Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks. We study the application of RAG to AI governance and policy analysis using the AI Governance and Regulatory Archive (AGORA) corpus, a curated collection of 947 AI policy documents. Our system combines a ColBERT-based retriever fine-tuned with contrastive learning and a generator aligned to human preferences using Direct Preference Optimization (DPO). We construct synthetic queries and collect pairwise preferences to adapt the system to the policy domain. Through experiments evaluating retrieval quality, answer relevance, and faithfulness, we find that domain-specific fine-tuning improves retrieval metrics but does not consistently improve end-to-end question answering performance. In some cases, stronger retrieval counterintuitively leads to more confident hallucinations when relevant documents are absent from the corpus. These results highlight a key concern for those building policy-focused RAG systems: improvements to individual components do not necessarily translate to more reliable answers. Our findings provide practical insights for designing grounded question-answering systems over dynamic regulatory corpora.

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HFEPX Relevance Assessment

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Trust level

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Eval-Fit Score

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Human Feedback Signal

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Evaluation Signal

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HFEPX Fit

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Field Provenance & Confidence

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Human Feedback Types

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Pairwise preference, Expert verification

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Evidence snippet: We construct synthetic queries and collect pairwise preferences to adapt the system to the policy domain.

Evaluation Modes

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Evidence snippet: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.

Quality Controls

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Evidence snippet: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.

Benchmarks / Datasets

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Evidence snippet: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.

Reported Metrics

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Evidence snippet: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.

Rater Population

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Unknown

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Evidence snippet: Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.

Human Data Lens

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  • Potential human-data signal: Pairwise preference, Expert verification
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  • Abstract highlights: 3 key sentence(s) extracted below.

Evaluation Lens

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Research Brief

Deterministic synthesis

Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.

Generated Mar 25, 2026, 5:54 PM · Grounded in abstract + metadata only

Key Takeaways

  • Retrieval-augmented generation (RAG) systems are increasingly used to analyze complex policy documents, but achieving sufficient reliability for expert usage remains challenging in domains characterized by dense legal language and evolving, overlapping regulatory frameworks.
  • We study the application of RAG to AI governance and policy analysis using the AI Governance and Regulatory Archive (AGORA) corpus, a curated collection of 947 AI policy documents.
  • Our system combines a ColBERT-based retriever fine-tuned with contrastive learning and a generator aligned to human preferences using Direct Preference Optimization (DPO).

Researcher Actions

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Caveats

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  • Signals below are heuristic and may miss details reported outside the abstract.

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