TextReasoningBench: Does Reasoning Really Improve Text Classification in Large Language Models?
arXiv:2603.19558v1 Announce Type: new Abstract: Eliciting explicit, step-by-step reasoning traces from large language models (LLMs) has emerged as a dominant paradigm for enhancing model capabilities. Although such reasoning strategies were originally designed for problems requiring explicit multi-step reasoning, they have increasingly been applied to a broad range of NLP tasks. This expansion implicitly assumes that deliberative reasoning uniformly benefits heterogeneous tasks. However, whether such reasoning mechanisms truly benefit classification tasks remains largely underexplored, especially considering their substantial token and time costs. To fill this gap, we introduce TextReasoningBench, a systematic benchmark designed to evaluate the effectiveness and efficiency of reasoning strategies for text classification with LLMs. We compare seven reasoning strategies, namely IO, CoT, SC-CoT, ToT, GoT, BoC, and long-CoT across ten LLMs on five text classification datasets. Beyond trad
arXiv:2603.19558v1 Announce Type: new Abstract: Eliciting explicit, step-by-step reasoning traces from large language models (LLMs) has emerged as a dominant paradigm for enhancing model capabilities. Although such reasoning strategies were originally designed for problems requiring explicit multi-step reasoning, they have increasingly been applied to a broad range of NLP tasks. This expansion implicitly assumes that deliberative reasoning uniformly benefits heterogeneous tasks. However, whether such reasoning mechanisms truly benefit classification tasks remains largely underexplored, especially considering their substantial token and time costs. To fill this gap, we introduce TextReasoningBench, a systematic benchmark designed to evaluate the effectiveness and efficiency of reasoning strategies for text classification with LLMs. We compare seven reasoning strategies, namely IO, CoT, SC-CoT, ToT, GoT, BoC, and long-CoT across ten LLMs on five text classification datasets. Beyond traditional metrics such as accuracy and macro-F1, we introduce two cost-aware evaluation metrics that quantify the performance gain per reasoning token and the efficiency of performance improvement relative to token cost growth. Experimental results reveal three notable findings: (1) Reasoning does not universally improve classification performance: while moderate strategies such as CoT and SC-CoT yield consistent but limited gains (typically +1% to +3% on big models), more complex methods (e.g., ToT and GoT) often fail to outperform simpler baselines and can even degrade performance, especially on small models; (2) Reasoning is often inefficient: many reasoning strategies increase token consumption by 10$\times$ to 100$\times$ (e.g., SC-CoT and ToT) while providing only marginal performance improvements.
Executive Summary
This article presents TextReasoningBench, a systematic benchmark designed to evaluate the effectiveness and efficiency of reasoning strategies for text classification with large language models (LLMs). The authors compare seven reasoning strategies across ten LLMs on five text classification datasets, introducing two cost-aware evaluation metrics to quantify performance gain per reasoning token and efficiency of performance improvement relative to token cost growth. The results reveal that reasoning does not universally improve classification performance and can be inefficient, increasing token consumption by up to 100 times while providing marginal performance improvements. The study highlights the need for a more nuanced understanding of the benefits and costs of reasoning strategies in LLMs, particularly in text classification tasks.
Key Points
- ▸ Reasoning does not universally improve classification performance in text classification tasks.
- ▸ Reasoning strategies can be inefficient, increasing token consumption by up to 100 times.
- ▸ More complex reasoning methods often fail to outperform simpler baselines and can degrade performance.
Merits
Strength in methodology
The authors introduce a systematic benchmark, TextReasoningBench, to evaluate the effectiveness and efficiency of reasoning strategies, providing a comprehensive and rigorous evaluation framework.
Novel evaluation metrics
The authors introduce two cost-aware evaluation metrics to quantify performance gain per reasoning token and efficiency of performance improvement relative to token cost growth, offering a more nuanced understanding of the benefits and costs of reasoning strategies.
Demerits
Limited task domain
The study focuses on text classification tasks, which may not be representative of other NLP tasks, and the findings may not generalize to other problem domains.
Scalability concerns
The study highlights the substantial token and time costs associated with reasoning strategies, which may limit their scalability and practical applicability in real-world settings.
Expert Commentary
The study presents a timely and thought-provoking critique of the current state of reasoning strategies in LLMs, highlighting the need for a more nuanced understanding of their benefits and costs. While the findings are limited to text classification tasks, they have broader implications for the development and deployment of NLP models in general. The authors' systematic benchmark and cost-aware evaluation metrics offer a valuable contribution to the field, and their recommendations for more efficient and scalable reasoning strategies are well-timed. However, the study's limitations in terms of task domain and scalability concerns should be carefully considered by practitioners and policymakers.
Recommendations
- ✓ Develop more efficient and scalable reasoning strategies that balance performance and efficiency.
- ✓ Explore the generalizability of the findings to other NLP tasks and problem domains.
Sources
Original: arXiv - cs.CL
