TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking

• URL: http://arxiv.org/abs/2508.09539v2
• Date: Tue, 19 Aug 2025 04:21:43 GMT
• Title: TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking
• Authors: Yongqi Fan, Xiaoyang Chen, Dezhi Ye, Jie Liu, Haijin Liang, Jin Ma, Ben He, Yingfei Sun, Tong Ruan,
• Abstract summary: Reasoning-intensive ranking models built on Large Language Models (LLMs) have made notable progress.<n>Existing approaches often rely on large-scale LLMs and explicit Chain-of-Thought (CoT) reasoning.<n>We propose textbfTFRank, an efficient pointwise reasoning ranker based on small-scale LLMs.
• Score: 21.930228130429573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reasoning-intensive ranking models built on Large Language Models (LLMs) have made notable progress, but 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 (e.g., 1.7B) 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.

Related papers

• RankLLM: Weighted Ranking of LLMs by Quantifying Question Difficulty [102.02839046225468]
  RankLLM is a novel framework designed to quantify both question difficulty and model competency.<n>We evaluate 30 models on 35,550 questions across multiple domains.
  arXiv  Detail & Related papers  (2026-02-12T21:28:46Z)
• Prism: Efficient Test-Time Scaling via Hierarchical Search and Self-Verification for Discrete Diffusion Language Models [96.0074341403456]
  Inference-time compute has re-emerged as a practical way to improve LLM reasoning.<n>Most test-time scaling (TTS) algorithms rely on autoregressive decoding.<n>We propose Prism, an efficient TTS framework for dLLMs.
  arXiv  Detail & Related papers  (2026-02-02T09:14:51Z)
• CoT Vectors: Transferring and Probing the Reasoning Mechanisms of LLMs [33.63911145333626]
  Chain-of-Thought prompting has emerged as a powerful approach to enhancing the reasoning capabilities of Large Language Models.<n>Existing implementations, such as in-context learning and fine-tuning, remain costly and inefficient.<n>We introduce CoT Vectors, compact representations that encode task-general, multi-step reasoning knowledge.
  arXiv  Detail & Related papers  (2025-10-01T06:58:23Z)
• ERank: Fusing Supervised Fine-Tuning and Reinforcement Learning for Effective and Efficient Text Reranking [33.25740773392183]
  ERank is a highly effective and efficient pointwise reranker built from a reasoning LLM that excels across diverse relevance scenarios.<n>We propose a novel two-stage training pipeline that begins with Supervised Fine-Tuning (SFT)<n>In this stage, we move beyond binary labels and train the model generatively to output fine grained integer scores, which significantly enhances relevance discrimination.<n>We evaluate the ERank reranker on the BRIGHT, FollowIR, TREC DL, and BEIR benchmarks, demonstrating superior effectiveness and robustness compared to existing approaches.
  arXiv  Detail & Related papers  (2025-08-30T14:56:53Z)
• ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability [41.99845885135309]
  We propose an automated reasoning-intensive training data synthesis framework.<n>A self-consistency data filtering mechanism is designed to ensure the data quality.<n>Our trained reasoning-intensive reranker textbfReasonRank achieves state-of-the-art (SOTA) performance 40.6 on the BRIGHT leaderboard.
  arXiv  Detail & Related papers  (2025-08-09T17:26:18Z)
• PixelThink: Towards Efficient Chain-of-Pixel Reasoning [70.32510083790069]
  PixelThink is a simple yet effective scheme that integrates externally estimated task difficulty and internally measured model uncertainty.<n>It learns to compress reasoning length in accordance with scene complexity and predictive confidence.<n> Experimental results demonstrate that the proposed approach improves both reasoning efficiency and overall segmentation performance.
  arXiv  Detail & Related papers  (2025-05-29T17:55:49Z)
• Reinforced Latent Reasoning for LLM-based Recommendation [83.18146814163308]
  Large Language Models (LLMs) have demonstrated impressive reasoning capabilities in complex problem-solving tasks.<n>Existing methods typically rely on fine-tuning with explicit chain-of-thought (CoT) data.<n>In this work, we explore an alternative approach that shifts from explicit CoT reasoning to compact, information-dense latent reasoning.
  arXiv  Detail & Related papers  (2025-05-25T11:03:45Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models [49.61246073215651]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex tasks.<n>Recent advancements in OpenAI o1 and DeepSeek-R1 have further improved performance in System-2 reasoning domains.<n>However, they also introduce significant computational overhead due to verbose and redundant outputs.
  arXiv  Detail & Related papers  (2025-03-20T17:59:38Z)
• Understanding Chain-of-Thought in LLMs through Information Theory [16.78730663293352]
  We formalize Chain-of-Thought (CoT) reasoning in Large Language Models (LLMs) through an information-theoretic lens.<n>Specifically, our framework quantifies the information-gain' at each reasoning step, enabling the identification of failure modes.<n>We demonstrate the efficacy of our approach through extensive experiments on toy arithmetic, GSM8K and PRM800k datasets.
  arXiv  Detail & Related papers  (2024-11-18T19:14:36Z)
• Self-Calibrated Listwise Reranking with Large Language Models [137.6557607279876]
  Large language models (LLMs) have been employed in reranking tasks through a sequence-to-sequence approach. This reranking paradigm requires a sliding window strategy to iteratively handle larger candidate sets. We propose a novel self-calibrated listwise reranking method, which aims to leverage LLMs to produce global relevance scores for ranking.
  arXiv  Detail & Related papers  (2024-11-07T10:31:31Z)
• Rational Metareasoning for Large Language Models [17.479428400594028]
  Being prompted to engage in reasoning has emerged as a core technique for using large language models (LLMs)<n>This work introduces a novel approach based on computational models of metareasoning used in cognitive science.<n>We develop a reward function that incorporates the Value of Computation by penalizing unnecessary reasoning.
  arXiv  Detail & Related papers  (2024-10-07T23:48:52Z)
• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models [35.17291316942284]
  We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise.
  arXiv  Detail & Related papers  (2023-10-14T05:20:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.