TFRank: Efficient LLM Relevance Ranking

• TFRank is a pointwise learning-to-rank framework that uses small-scale LLMs and CoT supervision for efficient and direct relevance scoring.
• It employs multi-task and fine-grained supervision with a five-level scoring system to merge reasoning-rich training with low-latency inference.
• TFRank achieves competitive performance on benchmarks like BRIGHT and BEIR while drastically reducing computational cost and latency in practical deployments.

TFRank is a pointwise learning-to-rank framework for relevance assessment that leverages small-scale LLMs to deliver efficient and practical ranking with internalized, “think-free” reasoning capabilities. The core innovation of TFRank lies in integrating chain-of-thought (CoT) data, fine-grained scoring, and multi-task supervision during training, allowing the model to achieve high accuracy without explicit reasoning text at inference. This approach enables direct output of relevance scores and reduces computational cost, bridging the gap between reasoning-rich LLM ranking and the latency requirements of real-world systems (Fan et al., 13 Aug 2025).

1. Architectural Overview and Motivation

TFRank is designed to overcome limitations present in recent LLM-based ranking systems, notably the high inference latency and prohibitive computational demands associated with explicit chain-of-thought (CoT) reasoning and large model sizes. While earlier approaches depend on multi-step, autoregressive reasoning—often with models at or above 7B parameters—to achieve high retrieval and re-ranking performance, TFRank produces scalar relevance scores pointwise, using small LLMs (e.g., 1.7B) while still taking advantage of advanced reasoning signals during training.

The “think-free reasoning” paradigm is achieved by two core strategies:

• Explicit CoT Supervision During Training, Think-Free Inference at Deployment: TFRank is exposed to explicit reasoning chains in its training data, but is prompted at test time to bypass reasoning and provide direct, short answers.
• Multi-task and Fine-Grained Supervision: Relevance is annotated on a five-level scale (0–4), and the model is trained under both pointwise, pairwise, and listwise ranking tasks—with pointwise output format enforced at inference.

This design allows TFRank to preserve the performance gains of reasoning-rich LLMs while maintaining throughput and scalability suitable for practical deployment.

2. Training Methodology and Think-Mode Switch

TFRank employs multi-task supervised fine-tuning (SFT), integrating several data sources:

• Chain-of-Thought (CoT) Data Distillation: For each (query, document) input, the training set supplies an explicit reasoning chain justifying the annotated score (applied to pointwise, pairwise, and listwise task variants).
• Fine-Grained Score Labels: Supervision uses a five-point scale (0–4) to communicate relevance granularity.
• Prompt Engineering for “Think-Mode Switch”: Inputs are prepended with either the “/think” or “/no think” mode-token. The “/think” mode triggers explicit reasoning and justification output; “/no think” instructs the model to provide a direct, score-annotated answer (e.g., yes(4), no(1)).

The training loss is the standard autoregressive LLMing objective:

$L_{SFT} = -\log P(T \mid C)$

where $C$ is the composed prompt (including the mode token, query, and document) and $T = \{t_1, \ldots, t_r\}$ is the target sequence (reasoning chain or score).

During inference, the switch to “/no think” prompts the model to suppress the reasoning chain and produce only the concise score label, drastically reducing inference output length and latency while preserving or improving ranking quality.

3. Comparative Performance: Efficiency and Effectiveness

TFRank is evaluated on both reasoning-intensive and general retrieval benchmarks:

• On BRIGHT (a reasoning-intensive benchmark), TFRank-1.7B achieves average NDCG@10 scores on par with or exceeding large baselines such as Rank1-7B and REARANK-7B, while using four times fewer parameters.
• On BEIR (a diverse retrieval benchmark), TFRank-8B achieves NDCG@10 (e.g., 43.2) that matches or surpasses state-of-the-art baselines.

The empirical evaluation demonstrates that the think-free inference mode yields nearly the same ranking quality as explicit CoT reasoning, while greatly enhancing throughput. For example, Figure 1 of (Fan et al., 13 Aug 2025) compares NDCG@10 versus the number of processed queries per hour, evidencing that pointwise “think-free” output enables an order-of-magnitude increase in inference speed compared to models requiring explicit CoT.

4. Practical Deployment and System Implications

TFRank’s pointwise “think-free” architecture provides substantial benefits for real-world deployment:

• Latency Reduction: By avoiding chain-of-thought generation at query time, each scoring output is reduced to a short label, resulting in far fewer output tokens.
• Parallelism: The pointwise design (scoring each candidate document independently) allows for high intra-query parallelism, further increasing throughput for large-scale search applications.
• Small Model Suitability: Effective performance is achieved on models as small as 1.7B parameters, reducing memory and compute requirements and enabling broader hardware compatibility.

This architecture effectively decouples CoT reasoning (needed for accurate semantic ranking) from runtime computational burden, presenting an attractive trade-off between interpretability (during training) and efficiency (during production inference).

5. Open Source Release and Community Resources

TFRank’s codebase and training data are released at [https://github.com/JOHNNY-fans/TFRank], enabling reproducibility and providing tools for further research in efficient reasoning-based ranking. Released resources include:

• Scripts and models for training and inference under both “/think” and “/no think” modes
• Implementations for multi-task fine-tuning using combinations of pointwise, pairwise, listwise, and CoT-supervised data
• Benchmarks and evaluation scripts for BRIGHT and BEIR datasets

This open dataset and code release facilitates both research and industrial adoption of efficient LLM-based ranking.

6. Context, Limitations, and Future Directions

TFRank addresses the critical challenge of integrating advanced LLM-based reasoning into search and ranking while maintaining deployment practicality. Its design demonstrates that reasoning can be internalized in model parameters via CoT supervision and later “compressed out” at inference to enable high-throughput, low-latency ranking without dependence on explicit reasoning output.

Observed limitations—such as the precise balance between explicit reasoning during training and inference efficiency, or potential score calibration issues between datasets—may warrant further paper. The architecture suggests promising directions:

• Further optimization of the think-mode switch for different ranking tasks
• Extension to larger model scales or multilingual ranking scenarios
• Investigation of score calibration and cross-dataset transfer of “think-free” trained rankers

7. Summary Table: Key Characteristics

Aspect	TFRank Approach	Impact
Model Type	Small-scale LLM (e.g., 1.7B, 8B params)	Reduced compute/memory
Training Supervision	Multi-task (pointwise, pairwise, listwise) + CoT + fine-grained score	Higher ranking fidelity
Inference Mode	Pointwise scoring (“/no think”)	Orders-of-magnitude faster than CoT
Output	Short score-annotated answer (e.g., yes(4))	Minimal output token budget
Benchmarks	BRIGHT, BEIR	State-of-the-art or competitive
Code/Data Release	https://github.com/JOHNNY-fans/TFRank	Community reproducibility, adoption

In summary, TFRank introduces an efficient architecture for integrating deep LLM-based reasoning into pointwise ranking, providing practical solutions for real-world ranking tasks with strong empirical support for its balance of effectiveness and efficiency (Fan et al., 13 Aug 2025).