Aura: Dynamic Intra-Turn Emotion-Aware Adaptation of Large Language Model Responses

2026-08-25

AI Summary

为解决大语言模型与用户交互时缺乏情感同步的问题，Aura框架通过感知用户情绪并采用低秩适应方法动态调整响应，提高学习效率和满意度。

Abstract

Effective human-AI interaction requires systems that dynamically adapt to a user's behavior and evolving understanding. When users interact with Large Language Models (LLMs), these models typically respond to prompts without sensing the user's immediate reactions. This lack of communicative synchrony can lead to information overload or leave confusion unresolved in real time. In this paper, we introduce Aura, a framework that enables LLM systems to dynamically modulate output based on a user's evolving emotions. Aura's Perception Module continuously estimates the user's emotional state from facial expressions. Our Policy Module then selects interventions through a probabilistic belief model. Finally, Aura's Generation Module uses parameter-efficient Low-Rank Adaptation (LoRA) adapters to produce contextually tailored responses mid-turn during response generation. We evaluated Aura in a within-subjects user study (N=20) on information-seeking tasks, where it achieved statistically significantly higher normalized perceived learning gains than a Llama-3 baseline and reduced interaction time by 21% relative to existing LLM baselines (GPT-4o, Llama-3). Our results indicate that real-time, context-sensitive interventions can improve learning efficiency and user satisfaction without observable degradation in factual accuracy. Aura thus supports the potential for more responsive and effective human-AI interaction.

Problem

• Large Language Models
• user's immediate reactions
• communicative synchrony
• information overload
• confusion

Innovation

• Dynamic Emotion-Aware Adaptation
• Low-Rank Adaptation (LoRA)
• Real-time Context-sensitive Interventions