3 papers
cs.IR2025
Harnessing Pairwise Ranking Prompting Through Sample-Efficient Ranking Distillation
Junru Wu, Le Yan, Zhen Qin +6
While Pairwise Ranking Prompting (PRP) with Large Language Models (LLMs) is one of the most effective zero-shot document ranking methods, it has a quadratic computational complexit…
cs.IR2025
MCRanker: Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
Fang Guo, Wenyu Li, Honglei Zhuang +5
The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results. However, these rankers are hindered by two major drawbacks: (1) they fail to…
cs.CL2025
Adapting Decoder-Based Language Models for Diverse Encoder Downstream Tasks
Paul Suganthan, Fedor Moiseev, Le Yan +7
Decoder-based transformers, while revolutionizing language modeling and scaling to immense sizes, have not completely overtaken encoder-heavy architectures in natural language proc…