TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918202845429760 |
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| author | Fan, Yongqi Chen, Xiaoyang Ye, Dezhi Liu, Jie Liang, Haijin Ma, Jin He, Ben Sun, Yingfei Ruan, Tong |
| author_facet | Fan, Yongqi Chen, Xiaoyang Ye, Dezhi Liu, Jie Liang, Haijin Ma, Jin He, Ben Sun, Yingfei Ruan, Tong |
| contents | 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 \textbf{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 ``\textbf{T}hink-\textbf{F}ree" 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. Our code and data are released in the repository: https://github.com/JOHNNY-fans/TFRank. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09539 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking Fan, Yongqi Chen, Xiaoyang Ye, Dezhi Liu, Jie Liang, Haijin Ma, Jin He, Ben Sun, Yingfei Ruan, Tong Information Retrieval 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 \textbf{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 ``\textbf{T}hink-\textbf{F}ree" 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. Our code and data are released in the repository: https://github.com/JOHNNY-fans/TFRank. |
| title | TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2508.09539 |