WARP: Wasserstein-Aligned RAG for Population Opinions
RAG systems are increasingly used to summarize what large collections of documents say. A user asks "What do people think about X?" and receives an answer that reads as consensus. But standard top-k retrieval ranks documents by query similarity, not by how faithfully they represent the population, so minority views quietly disappear. Existing fixes fall short. Diversity re-rankers like MMR and DPP spread retrieved documents apart, but with no target distribution to aim for. Calibration methods based on KL or JS divergence do target one, yet treat opinion bins as unordered: confusing strong positive with strong negative costs no more than an adjacent-bin miss. We introduce WARP, a family o...
| Authors | Aman Singh Thakur, Aditya Agrawal, Alwarappan Nakkiran et al. |
|---|---|
| Primary category | cs.IR |
| Published | 24 August 2026 |
| Last updated at source | 22 September 2026 |
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