Verbosity $\neq$ Veracity: Demystify Verbosity Compensation Behavior of Large Language Models

ICLR 2025 Conference Submission12365 Authors

Keywords: Large Language Models, Verbosity of LLM, LLM Uncertainty, LLM Routing

TL;DR: We define and categorize the verbose compensation behavior of large language models (LLMs), finding a significant performance gap associated with verbose responses, which we attribute to model uncertainty.

Abstract: Although Large Language Models (LLMs) have demonstrated their strong capabilities in various tasks, recent work has revealed LLMs also exhibit undesirable behaviors, such as hallucination and toxicity, limiting their reliability and broader adoption. In this paper, we discover an understudied type of undesirable behavior of LLMs, which we term **Verbosity Compensation (VC)** — similar to the hesitation behavior of humans under uncertainty — where they respond with excessive words such as repeating questions, introducing ambiguity, or providing excessive enumeration. We present the first work that defines and analyzes Verbosity Compensation, explores its causes, and proposes a simple mitigating approach. Our experiments, conducted on five datasets of knowledge and reasoning-based QA tasks with 14 newly developed LLMs, reveal three conclusions. 1) We reveal a pervasive presence of VC across all models and all datasets. Notably, GPT-4 exhibits a VC frequency of 50.40\%. 2) We reveal the large performance gap between verbose and concise responses, with a notable difference of 27.61\% on the Qasper dataset. We also demonstrate that this difference does not naturally diminish as LLM capability increases. Both 1) and 2) highlight the urgent need to mitigate the frequency of VC behavior and disentangle verbosity with veracity. We propose a simple yet effective cascade algorithm that replaces the verbose responses with the other model-generated responses. The results show that our approach effectively alleviates the VC of the Mistral model from 63.81\% to 16.16\% on the Qasper dataset. 3) We also find that verbose responses exhibit higher uncertainty across all five datasets, suggesting a strong connection between verbosity and model uncertainty. We will release our code and dataset upon acceptance.

Primary Area: foundation or frontier models, including LLMs

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Reciprocal Reviewing: I understand the reciprocal reviewing requirement as described on https://iclr.cc/Conferences/2025/CallForPapers. If none of the authors are registered as a reviewer, it may result in a desk rejection at the discretion of the program chairs. To request an exception, please complete this form at https://forms.gle/Huojr6VjkFxiQsUp6.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 12365