6 papers
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…
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…
Identifying Origins of Place Names via Retrieval Augmented Generation
Alexis Horde-Vo, Matt Duckham, Estrid He +1
Who is the "Batman" behind "Batman Street" in Melbourne? Understanding the historical, cultural, and societal narratives behind place names can reveal the rich context that has sha…
Evaluating Position Bias in Large Language Model Recommendations
Ethan Bito, Yongli Ren, Estrid He
Large Language Models (LLMs) are being increasingly explored as general-purpose tools for recommendation tasks, enabling zero-shot and instruction-following capabilities without th…
Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models
Riya Naik, Ashwin Srinivasan, Swati Agarwal +1
Many of us now treat LLMs as modern-day oracles asking it almost any kind of question. However, consulting an LLM does not have to be a single turn activity. But long multi-turn in…
An Empirical Study of the Role of Incompleteness and Ambiguity in Interactions with Large Language Models
Riya Naik, Ashwin Srinivasan, Estrid He +1
Natural language as a medium for human-computer interaction has long been anticipated, has been undergoing a sea-change with the advent of Large Language Models (LLMs) with startli…