Reasoning-intensive ranking models built on Large Language Models (LLMs) have made notable progress. However, existing approaches often rely on large-scale LLMs and explicit Chain-of-Thought (CoT) reasoning, resulting in high computational cost and latency that limit real-world use. To address this, we propose TFRank, an efficient pointwise reasoning ranker based on small-scale LLMs. To improve ranking performance, TFRank effectively integrates CoT data, fine-grained score supervision, and multi-task training. Furthermore, it achieves an efficient "Think-Free" reasoning capability by employing a "think-mode switch" and pointwise format constraints. Specifically, this allows the model to leverage explicit reasoning during training while delivering precise relevance scores for complex queries at inference without generating any reasoning chains. Experiments show that TFRank achieves performance comparable to models with four times more parameters on the BRIGHT benchmark, and demonstrates strong competitiveness on the BEIR benchmark. Further analysis shows that TFRank achieves an effective balance between performance and efficiency, providing a practical solution for integrating advanced reasoning into real-world systems.

The widespread adoption of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems is reshaping the landscape of information retrieval. However, the long-term effects of LLM-generated texts on retrieval systems remain underexplored, creating challenges for mitigating their impact. The effects are examined in this study, with a particular focus on the “Spiral of Silence” phenomenon, which refers to the marginalization of diverse information as certain types of content dominate, leading to a homogenized information ecosystem. To investigate this, a simulation pipeline is constructed to model the iterative introduction of LLM-generated texts into retrieval systems. Experimental results across multiple iterations reveal that as the presence of LLM-generated texts within the system grows, retrieval systems exhibit a stronger tendency to retrieve these texts. This trend, in turn, reduces the visibility of human-generated content, diminishes diversity, propagates errors, and results in a notable decline in retrieval performance. To address these challenges, we propose a Utility-Driven Multi-Objective Optimization (UMO) framework to effectively mitigate the “Spiral of Silence.” This framework employs a two-phase approach: an optimization phase, leveraging the NSGA-II algorithm to derive optimal preference weights for multiple objectives, and a memorization phase, which directly integrates these weights into the retrieval vector space without requiring additional model retraining. Experimental results demonstrate that this framework maintains stable retrieval effectiveness, improves the retrieval proportion of human-generated content, reduces the excessive influence of LLM-generated texts, and preserves information diversity, effectively mitigating the “Spiral of Silence.”

Entity matching (EM) is a critical step in entity resolution (ER). Recently, entity matching based on large language models (LLMs) has shown great promise. However, current LLM-based entity matching approaches typically follow a binary matching paradigm that ignores the global consistency among record relationships. In this paper, we investigate various methodologies for LLM-based entity matching that incorporate record interactions from different perspectives. Specifically, we comprehensively compare three representative strategies: matching, comparing, and selecting, and analyze their respective advantages and challenges in diverse scenarios. Based on our findings, we further design a compound entity matching framework (ComEM) that leverages the composition of multiple strategies and LLMs. ComEM benefits from the advantages of different sides and achieves improvements in both effectiveness and efficiency. Experimental results on 8 ER datasets and 10 LLMs verify the superiority of incorporating record interactions through the selecting strategy, as well as the further cost-effectiveness brought by ComEM.

Xiaoyang Chen, Ben He, Hongyu Lin, Xianpei Han, Tianshu Wang, Boxi Cao, Le Sun, Yingfei Sun. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.