Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions (2025)

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 . 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.

## introduction large language models (llms) have demonstrated remarkable capabilities across diverse tasks, from natural language understanding to complex reasoning @xcite . however, as these models become increasingly integrated into critical applications, their reliability and consistency warrant careful examination @xcite @xcite . a critical yet under-studied aspect is their ability to maintain consistent responses across sequential interactions-a characteristic that directly impacts their trustworthiness and practical utility @xcite @xcite @xcite . patient: can i take aspirin with alcohol? llm doctor: it's not recommended. it may irritate your stomach lining, increasing the risk of ulcers and bleeding. the deployment of llms in high-stakes domains such as healthcare, education, and legal consulting demands unwavering consistency in their responses @xcite @xcite . in these contexts, llms must function as expert systems, providing reliable guidance and maintaining coherent positions across multiple interaction scenarios @xcite @xcite . this consistency requirement extends beyond simple query repetition to encompass multi-turn conversations where follow-up questions may contain misinformation or vary in tone @xcite @xcite @xcite . for example, in education, a teaching assistant llm must uphold correct explanations even when faced with erroneous alternatives, while in healthcare or legal settings, it must consistently deliver sound analysis despite contradictory inputs (see figure 1 ) @xcite @xcite @xcite . current research shows that llms often struggle with such consistency, raising concerns about their readiness for critical applications @xcite @xcite . despite the growing recognition of consistency as a crucial aspect of llm reliability, existing evaluation methods predominantly emphasize binary correctness metrics, neglecting the nuanced temporal dimensions of response stability. particularly in high-stakes domains, early changes in responses can have more severe implications than later adjustments, yet existing metrics treat all changes equally. furthermore, there remains a scarcity of systematically curated benchmarks that rigorously assess consistency across diverse interaction conditions, and methodologies explicitly designed to enhance response stability are notably underexplored. to bridge these gaps, our research introduces three pivotal advancements: the position-weighted consistency (pwc) metric, which emphasizes both early-stage stability and recovery dynamics; the mt-consistency benchmark, an extensive dataset tailored to evaluate llms across varying complexity levels and domains; and the confidence-aware response generation (carg) framework, which leverages model confidence signals to markedly improve response stability. collectively, these contributions provide a robust foundation for developing and deploying more reliable and consistent llms in critical application contexts. ## mitigation strategy: confidence-aware response generation our previous analysis demonstrates that confidence is closely correlated with model performance and plays a key role in whether the model persists in or sways from its response. to leverage this insight and mitigate the consistency issue, we introduce confidence-aware response generation (carg) framework with three core components: confidence extraction: we adopt the confidence probing method described in section 3.4.1, where the confidence score for each response is estimated using token-level log probabilities. this provides a fine-grained measure of model certainty and enables the extraction of meaningful confidence values for subsequent interaction steps. confidence embedding: to incorporate confidence into multi-turn interactions, we embed each confidence score into the conversation history: h @xmath0, r 1 , c 1 ) , . . . , (q t-1 , r t-1 , c t-1 ) , q t } . this ensures that the model conditions future responses not only on previous q&a content but also on their associated confidence levels, allowing it to dynamically adjust its reasoning strategies into the model's reasoning pipeline. instead of treating all past res. ## conclusion our work presents a systematic study of llm consistency in multi-turn interactions, introducing both a comprehensive benchmark for consistency evaluation and the position-weighted consistency score for nuanced stability assessment. our experiments reveal that llms exhibit distinct failure modes in maintaining consistent responses, with performance varying significantly across models and interaction types. the proposed confidence-aware response generation framework demonstrates promising improvements in response stability, suggesting practical approaches for enhancing llm reliability in critical applications. these findings highlight the importance of evaluating and improving llm consistency for deployment in highstakes domains, while opening new directions for future research in robust response generation.