WARP: Wasserstein-Aligned RAG for Population Opinions

arXiv:2608.22859 · cs.IR, cs.CL · Submitted 2026-08-24 · Read on arXiv

cs.IR, cs.CL

Submitted: 2026-08-24

Updated: 2026-09-22

Terminology

• Calibration — 19× in this paper · explained in 7 episodes
• RAG — 9× in this paper · explained in 3 episodes
• Accuracy — 8× in this paper · explained in 1 episode
• Fidelity — 8× in this paper · explained in 2 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Optimal Transport — 6× in this paper · explained in 5 episodes
• Positional Bias — 5× in this paper · explained in 2 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Redundancy — 3× in this paper · explained in 2 episodes
• Trace — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Aggregation — 1× in this paper · explained in 1 episode
• BQP — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Distribution Matching — 1× in this paper · explained in 2 episodes
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• LLM judge — 1× in this paper · explained in 2 episodes
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Optimal Transport (OT) — 1× in this paper · explained in 4 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Wasserstein Distance — 1× in this paper · explained in 4 episodes

Sources

• Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
• The Amazon Nova Family of Models: Technical Report and Model Card
• Principled and Scalable Diversity-Aware Retrieval via Cardinality-Constrained Binary Quadratic Programming
• Wasserstein Measure Coresets
• DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
• MUSS: Multilevel Subset Selection for Relevance and Diversity
• AdaGReS:Adaptive Greedy Context Selection via Redundancy-Aware Scoring for Token-Budgeted RAG
• Result Diversification in Search and Recommendation: A Survey

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