Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions

• URL: http://arxiv.org/abs/2503.22353v4
• Date: Thu, 05 Jun 2025 08:39:20 GMT
• Title: Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions
• Authors: Yubo Li, Yidi Miao, Xueying Ding, Ramayya Krishnan, Rema Padman,
• Abstract summary: Large Language Models (LLMs) have shown remarkable capabilities across various tasks.<n>Their deployment in high-stake domains requires consistent and coherent behavior across multiple rounds of user interaction.<n>This paper introduces a comprehensive framework for evaluating and improving LLM response consistency.
• Score: 8.069858557211132
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have shown remarkable capabilities across various tasks, but their deployment in high-stake domains requires consistent and coherent behavior across multiple rounds of user interaction. This paper introduces a comprehensive framework for evaluating and improving LLM response consistency, making three key contributions. Code and data are available at: https://github.com/yubol-bobo/MT-Consistency. First, we introduce Position-Weighted Consistency (PWC), a metric designed to capture both the importance of early-stage stability and recovery patterns in multi-turn interactions. Second, we present MT-Consistency, a carefully curated benchmark dataset spanning diverse domains and difficulty levels, specifically designed to evaluate LLM consistency under various challenging follow-up scenarios. Third, we introduce Confidence-Aware Response Generation (CARG), a framework that significantly improves response stability by explicitly integrating internal model confidence scores during the generation process. Experimental results demonstrate that CARG significantly improves response stability without sacrificing accuracy, offering a practical path toward more dependable LLM behavior in critical, real-world deployments.

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