ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models

• URL: http://arxiv.org/abs/2510.14077v1
• Date: Wed, 15 Oct 2025 20:33:08 GMT
• Title: ERGO: Entropy-guided Resetting for Generation Optimization in Multi-turn Language Models
• Authors: Haziq Mohammad Khalid, Athikash Jeyaganthan, Timothy Do, Yicheng Fu, Sean O'Brien, Vasu Sharma, Kevin Zhu,
• Abstract summary: Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally.<n>We introduce ERGO, which quantifies internal uncertainty via Shannon entropy over next token distributions.<n> ERGO embraces variability in language and modeling, representing and responding to uncertainty.
• Score: 9.509429747913519
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) suffer significant performance degradation in multi-turn conversations when information is presented incrementally. Given that multi-turn conversations characterize everyday interactions with LLMs, this degradation poses a severe challenge to real world usability. We hypothesize that abrupt increases in model uncertainty signal misalignment in multi-turn LLM interactions, and we exploit this insight to dynamically realign conversational context. We introduce ERGO (Entropy-guided Resetting for Generation Optimization), which continuously quantifies internal uncertainty via Shannon entropy over next token distributions and triggers adaptive prompt consolidation when a sharp spike in entropy is detected. By treating uncertainty as a first class signal rather than a nuisance to eliminate, ERGO embraces variability in language and modeling, representing and responding to uncertainty. In multi-turn tasks with incrementally revealed instructions, ERGO yields a 56.6% average performance gain over standard baselines, increases aptitude (peak performance capability) by 24.7%, and decreases unreliability (variability in performance) by 35.3%, demonstrating that uncertainty aware interventions can improve both accuracy and reliability in conversational AI.

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