Verbosity Compensation Behavior

• Verbosity compensation behavior is the phenomenon where both human users and LLMs increase response length to manage uncertainty and cognitive load.
• It is measured using quantitative metrics such as verbosity bias scores, VC-Freq, and performance gaps, which guide evaluation and optimization strategies.
• Mitigation strategies include explicit length penalties, prompt engineering, and adaptive chain-of-thought selection to balance detailed reasoning with conciseness.

Verbosity compensation behavior denotes the systematic tendency for human users and artificial agents—especially LLMs—to adapt, inflate, or modulate the length of their utterances or chain-of-thought (CoT) traces in response to task difficulty, perceived uncertainty, alignment signals, or optimization artifacts. This phenomenon is observed across open-ended dialogue, reasoning, and preference-evaluation contexts. While some verbosity compensation is rational (e.g., elaboration under cognitive load), modern alignment and reward optimization pipelines often induce pathological compensation, where responses become unnecessarily verbose, redundant, or repetitive—sometimes as an unintended consequence of training protocols or model architecture.

1. Definition and Scope of Verbosity Compensation

Verbosity compensation refers to the deliberate or involuntary increase in utterance or CoT length in response to non-length-centric demands. The paradigmatic cases fall into two broad families:

• Human dialogue: Users facing more intimate, emotionally charged, or cognitively taxing topics tend to expand their utterance length, either to provide context, reduce ambiguity, or work through uncertainty (Razavi et al., 2019).
• LLMs: LLMs, especially those trained with reinforcement learning from human or AI feedback (RLHF/RLAIF), direct preference optimization (DPO), or supervised fine-tuning (SFT) on long CoT traces, routinely compensate for uncertainty or optimization signals by generating unnecessarily lengthy, redundant, or circuitously reasoned responses (Saito et al., 2023, Zhang et al., 12 Nov 2024, Zhang et al., 12 May 2025, Lee et al., 3 Mar 2025, Cai et al., 16 May 2025, Lu et al., 16 Jun 2024, Park et al., 28 Mar 2024, Hong et al., 4 Aug 2025).

In the LLM setting, verbosity compensation manifests as:

• Compressible overlong responses even under explicit conciseness instructions (“answer as concisely as possible”),
• Length inflation on harder problems or under high uncertainty,
• Redundant chain-of-thought steps and internal/external CoT redundancy,
• Exploitation of length-related artifacts in reward functions or alignment losses.

2. Quantitative Metrics and Formalizations

Multiple frameworks quantify verbosity compensation, enabling both descriptive characterization and algorithmic mitigation.

General-LLM and Task-Specific Metrics

• Verbosity bias score (Saito et al., 2023): For preference labeling, the signed difference in LLM–human agreement rates, conditional on whether the human-preferred answer is shorter or longer:

$$\text{VerbosityBias} = P(Y' \neq Y \mid S = 1-Y) - P(Y' \neq Y \mid S = Y)$$

where $Y$ is the human label, $Y'$ is the LLM’s choice, and $S$ marks whether the longer answer was chosen.

• VC frequency (Zhang et al., 12 Nov 2024): For a dataset $D$ and detector $V(x, y, r)$, the fraction of responses flagged verbose:

$$\text{VC-Freq}(D) = \frac{1}{|D|} \sum_{(x,q,y)\in D} V(x,q,y,r)$$

with $V=1$ iff $r$ could be compressed without losing meaning, per a concise baseline.

• Performance gap (Δ): Difference in task performance (e.g., answer recall) between concise and verbose responses, as well as its normalized variant (Zhang et al., 12 Nov 2024).
• Average per-turn word count (Razavi et al., 2019): Mean $V_i = (1/N_i) \sum_{j=1}^{N_i} w_{ij}$, analyzed by topic class or session index.
• Redundancy indices (Hong et al., 4 Aug 2025):
  • External redundancy degree (ERD): the fraction of CoT tokens after the first correct solution (FCS).
  • Internal redundancy degree (IRD): mean cosine similarity of overlapping semantic segments within FCS.

Fine-Grained Annotations

• Reasoning Verbosity (RV) and Cognitive Difficulty (CD) (Cai et al., 16 May 2025): RV quantifies “wordiness” (0–9, jointly judged by LLM rubric and normalized length), while CD quantifies depth/sophistication (0–9, rubric-based LLM annotation). These enable precise measurement of how CoT length correlates to task complexity across 2M annotated reasoning tasks.
• Token complexity ($C^*(q)$) (Lee et al., 3 Mar 2025): For a given problem $q$, $C^*(q)$ indicates the minimum tokens needed for high-probability correctness, serving as an information-theoretic lower bound for chain length under accuracy constraints.

3. Mechanisms and Causes of Verbosity Compensation

Human and Social

Humans compensate verbosity as a response to social, emotional, or cognitive challenge. Increased task difficulty, intimacy, or risk of misunderstanding elevate average per-turn word count, both across topics and over longitudinal interaction (Razavi et al., 2019). This compensation is adaptive—serving to cushion uncertainty or enhance rapport—and increases with experience as comfort with the dialogue agent grows.

Model-Intrinsic and Optimization-Induced

For LLMs, several mechanisms drive verbosity compensation:

• Uncertainty-induced elaboration: With high perplexity or ambiguous contexts, models mimic human hesitation, adding repetitive or generic content despite concise instructions (Zhang et al., 12 Nov 2024).
• Reward/model bias: RLHF and LLM judge-based preference models are often biased toward longer completions, particularly if human or AI preference labels are skewed (Saito et al., 2023, Zhang et al., 12 Nov 2024).
• Algorithmic exploitation: DPO and related algorithms exhibit “length bias”—because sequence-level KL or implicit reward signals scale with output length, longer generations receive excessive gradient updates, giving rise to unbounded verbosity and reward hacking if unmitigated (Lu et al., 16 Jun 2024, Park et al., 28 Mar 2024). Out-of-distribution bootstrapping further exaggerates this effect: as DPO-trained policies venture away from the training set, the reward becomes increasingly correlated with length.
• Trace construction from SFT and CoT: Small models SFT-ed on long traces overgenerate when they lack signal to decide when to stop, especially on errors, yielding substantial verboseness in failed chains (Zhang et al., 12 May 2025, Lee et al., 3 Mar 2025).
• Mismatch of reasoning level and CoT verbosity: When CD (difficulty) and RV (verbosity) are mismatched—e.g. overly verbose chains on routine tasks or terse chains for complex ones—model performance degrades (Cai et al., 16 May 2025). Natural compensation does occur but with only modest adaptivity relative to a static baseline (Lee et al., 3 Mar 2025).

4. Diagnostics and Empirical Findings

Prevalence, Magnitude, and Task Dependence

Empirical studies consistently demonstrate high rates of verbosity compensation across model families, tasks, and evaluation frameworks:

• All major LLMs exhibit measurable verbosity compensation, with VC frequencies ranging from 13.6% (Llama-3-70B) to 74% (Mistral-7B) depending on task and prompting (Zhang et al., 12 Nov 2024).
• The performance gap between concise and verbose responses can reach double-digit percentages across datasets, with recall often dropping by 20–30% in the verbose subset (Zhang et al., 12 Nov 2024).
• In preference labeling, GPT-4’s tendency to prefer longer completions is substantial, with a signed verbosity-bias score of 0.328 on the HH-RLHF dataset (GPT-3.5: 0.428), compared to much smaller human biases (Saito et al., 2023).
• In supervised small models, the mean token length of incorrect responses exceeds that of correct responses by hundreds to thousands of tokens, with repeat rates (excessively repetitive traces) exceeding 60% in some SFT-trained SLMs (Zhang et al., 12 May 2025).
• Analysis of CoTs in OmniThought confirms that higher cognitive difficulty tasks result in longer, more elaborate reasoning traces (higher RV) (Cai et al., 16 May 2025).
• Studies of dialogue with older adults show substantial increases in average turn length for difficult or emotionally intense topics (effect sizes ~25 words per turn, r up to .94 for time-progression effects) (Razavi et al., 2019).

Compensation and Adaptivity

While both humans and LLMs exhibit adaptivity—adjusting verbosity upwards with task difficulty—the adaptivity in LLMs is only partial:

• Average chain lengths are lower on “easy” than “hard” problems (Lee et al., 3 Mar 2025, Cai et al., 16 May 2025).
• Measured Kendall-τ correlations between actual complexity and generated length range 0.04–0.53, well below perfect adaptivity (Lee et al., 3 Mar 2025).
• A fixed-length baseline approximates current model adaptivity, suggesting limited context-sensitive adjustment.

Redundancy Phenomena

Disentangling verbosity compensation requires separating functional from wasteful verbosity:

• External redundancy: Additional tokens beyond the first correct solution (FCS) are nearly always superfluous and can be eliminated without degrading accuracy (Hong et al., 4 Aug 2025).
• Internal redundancy: Within the FCS, some redundancy supports coherence; over-zealous pruning impairs correctness (internal redundancy degree IRD must be cautiously regulated).

5. Model- and Data-Driven Mitigation Strategies

Reward-Level and Training Interventions

• Explicit length penalties: Adding a regularization term $-a\,\mathbb{E}[|y|]$ to reward formulations or using length-margin terms in DPO objectives sharply reduces verbosity exploitation, restoring length–quality independence (Park et al., 28 Mar 2024, Lu et al., 16 Jun 2024).
• Token-level normalization or down-sampling: Using per-token or down-sampled sequence-level KL divergence (SamPO) neutralizes length artifacts in DPO, yielding 5–12% accuracy/win-rate gains and restraining generation length (Lu et al., 16 Jun 2024).
• Dual-penalty semantic RL: Penalizing both internal and external redundancy in chain-of-thought traces via semantic similarity and FCS analysis produces concise reasoning with minimal loss of accuracy (Hong et al., 4 Aug 2025).

Prompting, Calibration, and CoT Construction

• Prompt engineering: Repeated, explicit instructions for conciseness reduce but do not eliminate VC (Zhang et al., 12 Nov 2024, Saito et al., 2023).
• Cascade/routing ensembles: Escalating to progressively stronger models when verbose/low-confidence responses are detected can cut short unnecessary verbosity and improve performance (Zhang et al., 12 Nov 2024).
• Temperature scaling: Directly modulating EOS token probability trims SLM-generated CoT traces by up to 50%, with negligible or improved accuracy (Zhang et al., 12 May 2025).
• RV/CD-based CoT selection: Filtering chains to match model capability (by difficulty and verbosity) yields superior performance relative to random or generic selection (Cai et al., 16 May 2025).

Evaluation Transparency and Best Practices

• Bias monitoring: Accompany preference judgments or alignment evaluations with empirical verbosity-bias curves and statistically controlled comparisons (Saito et al., 2023).
• Adaptive prompting: Develop verifying/routing protocols that allocate longer chains only to high token-complexity questions, approaching optimal length–accuracy trade-offs (Lee et al., 3 Mar 2025, Cai et al., 16 May 2025).

6. Broader Implications and Future Research Directions

Verbosity compensation behavior reveals that both humans and LLMs adapt utterance length based on informational demand and alignment signals, but that model-centric optimization pipelines often induce pathological compensation. Emerging trends in alignment, direct preference optimization, and semantic RL highlight both algorithms’ sensitivity to auxiliary features such as output length and the benefits of disentangling factual content from mere verbosity.

Key open questions include:

• Robust prediction of token complexity for adaptive chain construction (Lee et al., 3 Mar 2025).
• Instance-adaptive thresholds for internal redundancy, balancing coherence and conciseness (Hong et al., 4 Aug 2025).
• Reliable transfer of compensation control to multilingual, multimodal, and retrieval-augmented agents.
• Integration of semantic, redundancy-aware rewards into mainstream RLHF/RLAIF pipelines.
• Tracking and mitigating OOD bootstrapping and other forms of Goodhart’s-law failures in implicit-reward alignment setups (Park et al., 28 Mar 2024).

A consensus is forming around the necessity of fine-grained length and redundancy regulation, fine-grained annotation of CoT traces, and transparency in evaluation to prevent spurious performance inflation through verbosity. Novel benchmarks such as OmniThought, algorithmic advances in DPO regularization, and multi-objective RL approaches are converging toward token-efficient, semantically controlled reasoning in advanced LLMs and interactive systems.