3 papers
cs.IR2026
Position Bias Undermines Preference Consistency in Listwise LLM-Based Reranking
Ethan Bito, Yongli Ren, Estrid He
Large language models (LLMs) have emerged as promising listwise rerankers for recommender systems, but their reliability under equivalent candidate permutations remains unclear. Si…
cs.IR2026
Verifiable User Simulation for Search and Recommendation Systems
Chenglong Ma, Xinye Wanyan, Danula Hettiachchi +3
Large-language-model (LLM) based user simulation is increasingly adopted for evaluating search engines, recommender systems, and retrieval-augmented generation pipelines, yet most…
cs.IR2026
One Pass, Any Order: Position-Invariant Listwise Reranking for LLM-Based Recommendation
Ethan Bito, Yongli Ren, Estrid He
Large language models (LLMs) are increasingly used for recommendation reranking, but their listwise predictions can depend on the order in which candidates are presented. This crea…