Task Complexity Matters: An Empirical Study of Reasoning in LLMs for Sentiment Analysis

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.

💡 Research Summary

This paper rigorously investigates whether the widely‑promoted claim that “reasoning improves performance across all language tasks” holds for sentiment analysis, a foundational NLP application. The authors evaluate 504 configurations spanning seven model families—DeepSeek‑R1 (full and distilled), DeepSeek‑V3 (base), LLaMA (3.1‑8B to 3.3‑70B), Qwen2.5 (14B & 32B), Qwen3 (4B‑32B, with both “thinking” and “non‑thinking” modes), Granite3.3 (2B & 8B, conditional reasoning), and Magistral (24B, RL‑based reasoning). Each family is tested on three sentiment benchmarks that differ in granularity: IMDB (binary), Amazon Reviews (five‑class), and GoEmotions (27‑class single‑label). The number of target classes serves as a proxy for task complexity.

Experimental settings include zero‑shot and few‑shot prompting (0, 5, 10, 20, 30, 40, 50 examples) with balanced sampling (seed 42). All models receive an identical system prompt that asks them to output a JSON object containing a sentiment label and a brief explanation; the scale is adapted to each dataset (2‑point, 5‑point, or 27‑point). Performance is measured by F1 (binary or weighted for multi‑class) and efficiency by mean per‑sample latency on an NVIDIA H100 GPU.

Key findings:

1. Task‑complexity dependence – Reasoning (thinking) helps only on the most complex task. On IMDB, reasoning degrades F1 by up to ‑19.9 percentage points (pp); on Amazon it drops by up to ‑18.4 pp; on GoEmotions it improves by up to +16.0 pp. Aggregated across all configurations, base/non‑thinking models outperform reasoning/distilled models on simple tasks (‑4.8 pp on IMDB, ‑3.6 pp on Amazon) but lose on the 27‑class task (+2.0 pp).

2. Distilled vs. full reasoning models – In zero‑shot, distilled reasoning models are consistently worse than their base counterparts, especially on low‑complexity data (e.g., DSR1 vs. DSV3 on IMDB: ‑19.9 pp). Few‑shot examples narrow the gap, and on GoEmotions distilled models even surpass bases (+3.2 to +4.8 pp).

3. Few‑shot advantage – Adding exemplars improves performance for almost all models, but the benefit is larger for non‑thinking models. For some reasoning models (e.g., Magistral‑24B on Amazon), few‑shot flips a positive gain (+12.2 pp) into a slight loss (‑0.9 pp).

4. Failure rates – Reasoning models underperform their non‑thinking peers in 100 % of binary comparisons, 80 % of five‑class, and 50 % of 27‑class cases, confirming that “over‑deliberation” harms simple classification.

5. Efficiency trade‑offs – Reasoning incurs 2.5×‑54× higher latency. Speed ratios are highest on binary tasks (up to 20.9×) and moderate on complex emotion recognition (4.3×‑6.2×). Pareto frontier analysis shows base models dominate the efficiency‑performance space; reasoning is justified only when the task’s complexity warrants the extra compute.

6. Qualitative error analysis – The authors observe systematic over‑generation of chain‑of‑thought explanations that dilute the core sentiment cue, leading to misclassifications on simple datasets. This provides a mechanistic explanation for the “overthinking” hypothesis.

Practical implications: For real‑time sentiment services, deploying reasoning‑enhanced LLMs is rarely cost‑effective; few‑shot prompting and larger base models deliver better accuracy‑efficiency balances. Reasoning becomes worthwhile only for fine‑grained emotion detection where the label space is large and the decision boundary is nuanced.

Limitations include reliance on class count as the sole complexity metric (potential confounding domain effects) and a binary “thinking vs. non‑thinking” categorization that does not explore intermediate prompting strategies or chain length controls.

In sum, the study overturns the universal reasoning‑improvement narrative for sentiment analysis, demonstrating that reasoning’s benefits are contingent on task complexity and come with substantial computational overhead. The released code, prompts, and results provide a valuable benchmark for future work on cost‑aware reasoning in LLMs.