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

• URL: http://arxiv.org/abs/2411.07858v2
• Date: Sat, 07 Dec 2024 22:11:27 GMT
• Title: Verbosity $\neq$ Veracity: Demystify Verbosity Compensation Behavior of Large Language Models
• Authors: Yusen Zhang, Sarkar Snigdha Sarathi Das, Rui Zhang,
• Abstract summary: We discover an understudied type of undesirable behavior of Large Language Models (LLMs)<n>We term Verbosity Compensation (VC) as similar to the hesitation behavior of humans under uncertainty.<n>We propose a simple yet effective cascade algorithm that replaces verbose responses with the other model-generated responses.
• Score: 8.846200844870767
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Our dataset and code are available at https://github.com/psunlpgroup/VerbosityLLM.

Related papers

• Video SimpleQA: Towards Factuality Evaluation in Large Video Language Models [69.68265487134686]
  Video SimpleQA is the first comprehensive benchmark tailored for factuality evaluation of LVLMs. Our work distinguishes from existing video benchmarks through the following key features. Answers are crafted as unambiguous and definitively correct in a short format.
  arXiv  Detail & Related papers  (2025-03-24T17:46:09Z)
• Beyond Words: How Large Language Models Perform in Quantitative Management Problem-Solving [0.0]
  This study examines how Large Language Models (LLMs) perform when tackling quantitative management decision problems in a zero-shot setting. We generated 900 responses generated by five leading models across 20 diverse managerial scenarios.
  arXiv  Detail & Related papers  (2025-02-23T12:39:39Z)
• Asking Again and Again: Exploring LLM Robustness to Repeated Questions [0.0]
  We evaluate five recent large language models (LLMs) on reading comprehension datasets. Our results demonstrate that question repetition can increase models' accuracy by up to $6%$. Across all models, settings, and datasets, we do not find the result statistically significant.
  arXiv  Detail & Related papers  (2024-12-10T21:09:12Z)
• LLM Robustness Against Misinformation in Biomedical Question Answering [50.98256373698759]
  The retrieval-augmented generation (RAG) approach is used to reduce the confabulation of large language models (LLMs) for question answering. We evaluate the effectiveness and robustness of four LLMs against misinformation in answering biomedical questions.
  arXiv  Detail & Related papers  (2024-10-27T16:23:26Z)
• A Debate-Driven Experiment on LLM Hallucinations and Accuracy [7.821303946741665]
  This study investigates the phenomenon of hallucination in large language models (LLMs) Multiple instances of GPT-4o-Mini models engage in a debate-like interaction prompted with questions from the TruthfulQA dataset. One model is deliberately instructed to generate plausible but false answers while the other models are asked to respond truthfully.
  arXiv  Detail & Related papers  (2024-10-25T11:41:27Z)
• Optimizing Language Model's Reasoning Abilities with Weak Supervision [48.60598455782159]
  We present textscPuzzleBen, a weakly supervised benchmark that comprises 25,147 complex questions, answers, and human-generated rationales. A unique aspect of our dataset is the inclusion of 10,000 unannotated questions, enabling us to explore utilizing fewer supersized data to boost LLMs' inference capabilities.
  arXiv  Detail & Related papers  (2024-05-07T07:39:15Z)
• Groundedness in Retrieval-augmented Long-form Generation: An Empirical Study [61.74571814707054]
  We evaluate whether every generated sentence is grounded in retrieved documents or the model's pre-training data. Across 3 datasets and 4 model families, our findings reveal that a significant fraction of generated sentences are consistently ungrounded. Our results show that while larger models tend to ground their outputs more effectively, a significant portion of correct answers remains compromised by hallucinations.
  arXiv  Detail & Related papers  (2024-04-10T14:50:10Z)
• R-Tuning: Instructing Large Language Models to Say `I Don't Know' [66.11375475253007]
  Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. Previous instruction tuning methods force the model to complete a sentence no matter whether the model knows the knowledge or not. We present a new approach called Refusal-Aware Instruction Tuning (R-Tuning) Experimental results demonstrate R-Tuning effectively improves a model's ability to answer known questions and refrain from answering unknown questions.
  arXiv  Detail & Related papers  (2023-11-16T08:45:44Z)
• Are You Sure? Challenging LLMs Leads to Performance Drops in The FlipFlop Experiment [82.60594940370919]
  We propose the FlipFlop experiment to study the multi-turn behavior of Large Language Models (LLMs) We show that models flip their answers on average 46% of the time and that all models see a deterioration of accuracy between their first and final prediction, with an average drop of 17% (the FlipFlop effect) We conduct finetuning experiments on an open-source LLM and find that finetuning on synthetically created data can mitigate - reducing performance deterioration by 60% - but not resolve sycophantic behavior entirely.
  arXiv  Detail & Related papers  (2023-11-14T23:40:22Z)
• Rephrase, Augment, Reason: Visual Grounding of Questions for Vision-Language Models [59.05769810380928]
  Rephrase, Augment and Reason (RepARe) is a gradient-free framework that extracts salient details about the image using the underlying vision-language model. We show that RepARe can result in a 3.85% (absolute) increase in zero-shot accuracy on VQAv2, 6.41%, and 7.94% points increase on A-OKVQA, and VizWiz respectively.
  arXiv  Detail & Related papers  (2023-10-09T16:57:57Z)
• Improving the Reliability of Large Language Models by Leveraging Uncertainty-Aware In-Context Learning [76.98542249776257]
  Large-scale language models often face the challenge of "hallucination" We introduce an uncertainty-aware in-context learning framework to empower the model to enhance or reject its output in response to uncertainty.
  arXiv  Detail & Related papers  (2023-10-07T12:06:53Z)
• Are Large Language Models Really Robust to Word-Level Perturbations? [68.60618778027694]
  We propose a novel rational evaluation approach that leverages pre-trained reward models as diagnostic tools. Longer conversations manifest the comprehensive grasp of language models in terms of their proficiency in understanding questions. Our results demonstrate that LLMs frequently exhibit vulnerability to word-level perturbations that are commonplace in daily language usage.
  arXiv  Detail & Related papers  (2023-09-20T09:23:46Z)
• Evaluating Large Language Models on Graphs: Performance Insights and Comparative Analysis [7.099257763803159]
  We evaluate the capabilities of four Large Language Models (LLMs) in addressing several analytical problems with graph data. We employ four distinct evaluation metrics: Correctness, Fidelity, and Rectification. GPT models can generate logical and coherent results, outperforming alternatives in correctness.
  arXiv  Detail & Related papers  (2023-08-22T06:32:07Z)
• Can Large Language Models Infer Causation from Correlation? [104.96351414570239]
  We test the pure causal inference skills of large language models (LLMs) We formulate a novel task Corr2Cause, which takes a set of correlational statements and determines the causal relationship between the variables. We show that these models achieve almost close to random performance on the task.
  arXiv  Detail & Related papers  (2023-06-09T12:09:15Z)
• Explanation-based Finetuning Makes Models More Robust to Spurious Cues [21.327036110196637]
  Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task. We propose explanation-based finetuning as a general approach to mitigate LLMs' reliance on spurious correlations. We finetune the model to additionally generate a free-text explanation supporting its answer.
  arXiv  Detail & Related papers  (2023-05-08T18:53:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.