Retrieval Improvements Do Not Guarantee Better Answers: A Study of RAG for AI Policy QA
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 a...
| Authors | Saahil Mathur, Ryan David Rittner, Vedant Ajit Thakur et al. |
|---|---|
| Primary category | cs.CL |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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