Multi-Conditional Ranking with Large Language Models

arXiv:2404.00211v1 Announce Type: cross
Abstract: Utilizing large language models (LLMs) to rank a set of items has become a common approach in recommendation and retrieval systems. Typically, these systems focus on ordering a substantial number of documents in a monotonic order based on a given query. However, real-world scenarios often present a different challenge: ranking a comparatively smaller set of items, but according to a variety of diverse and occasionally conflicting conditions. In this paper, we define and explore the …

abstract arxiv become challenge cs.cl cs.lg documents focus however language language models large language large language models llms query ranking recommendation retrieval set systems type world