Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis
arXiv:2602.24060v1 Announce Type: new Abstract: Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families--including adaptive, conditional, and reinforcement learning-based reasoning architectures--on sentiment analysis datasets of varying granularity (binary, five-class, and 27-class emotion). Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions: (1) Reasoning shows task-complexity dependence--binary classification degrades up to -19.9 F1 percentage points (pp), while 27-class emotion recognition gains up to +16.0pp; (2) Distilled reasoning variants underperform base models by 3-18 pp on simpler tasks, though few-shot prompting enables partial recovery; (3) Few-shot learning improves over zero-shot in most cases regardle
arXiv:2602.24060v1 Announce Type: new Abstract: Large language models (LLMs) with reasoning capabilities have fueled a compelling narrative that reasoning universally improves performance across language tasks. We test this claim through a comprehensive evaluation of 504 configurations across seven model families--including adaptive, conditional, and reinforcement learning-based reasoning architectures--on sentiment analysis datasets of varying granularity (binary, five-class, and 27-class emotion). Our findings reveal that reasoning effectiveness is strongly task-dependent, challenging prevailing assumptions: (1) Reasoning shows task-complexity dependence--binary classification degrades up to -19.9 F1 percentage points (pp), while 27-class emotion recognition gains up to +16.0pp; (2) Distilled reasoning variants underperform base models by 3-18 pp on simpler tasks, though few-shot prompting enables partial recovery; (3) Few-shot learning improves over zero-shot in most cases regardless of model type, with gains varying by architecture and task complexity; (4) Pareto frontier analysis shows base models dominate efficiency-performance trade-offs, with reasoning justified only for complex emotion recognition despite 2.1x-54x computational overhead. We complement these quantitative findings with qualitative error analysis revealing that reasoning degrades simpler tasks through systematic over-deliberation, offering mechanistic insight beyond the high-level overthinking hypothesis.
Executive Summary
The article challenges the prevailing assumption that reasoning universally improves performance in large language models (LLMs) across language tasks. Through a comprehensive evaluation of 504 configurations, the study reveals that reasoning effectiveness is strongly task-dependent, with binary classification tasks experiencing a decline in performance and complex emotion recognition tasks showing improvement. The findings highlight the importance of considering task complexity when evaluating the effectiveness of reasoning in LLMs.
Key Points
- ▸ Reasoning effectiveness is task-dependent, with varying performance across binary, five-class, and 27-class emotion recognition tasks
- ▸ Distilled reasoning variants underperform base models on simpler tasks, while few-shot prompting enables partial recovery
- ▸ Few-shot learning improves over zero-shot in most cases, with gains varying by architecture and task complexity
Merits
Comprehensive Evaluation
The study's comprehensive evaluation of 504 configurations across seven model families provides a thorough understanding of the relationship between task complexity and reasoning effectiveness
Demerits
Limited Generalizability
The study's focus on sentiment analysis datasets may limit the generalizability of the findings to other language tasks
Expert Commentary
The study's findings have significant implications for the development and evaluation of LLMs with reasoning capabilities. The results highlight the importance of considering task complexity and the potential trade-offs between performance and computational overhead. The study's qualitative error analysis provides valuable insight into the mechanisms underlying the degradation of simpler tasks, and the findings have important implications for the development of more efficient and effective LLMs. Furthermore, the study's results underscore the need for task-specific evaluation and validation of LLMs, and regulators should be aware of the potential limitations of these models.
Recommendations
- ✓ Developers should prioritize task-specific evaluation and validation of LLMs with reasoning capabilities
- ✓ Regulators should establish guidelines for the development and evaluation of LLMs with reasoning capabilities, considering task complexity and computational overhead