TFRank: Think-Free Reasoning Enables Practical Pointwise LLM Ranking

Understand

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.