3 papers
cs.CL2026
DoubtLess: Training-Free Contrastive Decoding for Efficient Reasoning
Minghao Luo, Liang Chen
Reasoning models achieve strong performance through long chains of thought (CoT), but often spend more tokens than needed to solve a problem. Existing ways to shorten the CoT requi…
cs.CL2026
One Polluted Page Is Enough: Evaluating Web Content Pollution in LLM Recommenders
Minghao Luo, Liang Chen
Search-augmented LLMs increasingly mediate everyday consumer recommendations by retrieving live web content. This creates a new risk: LLM recommenders may consume web content that…
cs.IR2023
Self-Sampling Training and Evaluation for the Accuracy-Bias Tradeoff in Recommendation
Dugang Liu, Yang Qiao, Xing Tang +4
Research on debiased recommendation has shown promising results. However, some issues still need to be handled for its application in industrial recommendation. For example, most o…