Don't Get Too Excited -- Eliciting Emotions in LLMs

• URL: http://arxiv.org/abs/2503.02457v1
• Date: Tue, 04 Mar 2025 10:06:41 GMT
• Title: Don't Get Too Excited -- Eliciting Emotions in LLMs
• Authors: Gino Franco Fazzi, Julie Skoven Hinge, Stefan Heinrich, Paolo Burelli,
• Abstract summary: This paper investigates the challenges of affect control in large language models (LLMs)<n>We evaluate state-of-the-art open-weight LLMs to assess their affective expressive range.<n>We quantify the models' capacity to express a wide spectrum of emotions and how they fluctuate during interactions.
• Score: 1.8399318639816038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the challenges of affect control in large language models (LLMs), focusing on their ability to express appropriate emotional states during extended dialogues. We evaluated state-of-the-art open-weight LLMs to assess their affective expressive range in terms of arousal and valence. Our study employs a novel methodology combining LLM-based sentiment analysis with multiturn dialogue simulations between LLMs. We quantify the models' capacity to express a wide spectrum of emotions and how they fluctuate during interactions. Our findings reveal significant variations among LLMs in their ability to maintain consistent affect, with some models demonstrating more stable emotional trajectories than others. Furthermore, we identify key challenges in affect control, including difficulties in producing and maintaining extreme emotional states and limitations in adapting affect to changing conversational contexts. These findings have important implications for the development of more emotionally intelligent AI systems and highlight the need for improved affect modelling in LLMs.

Related papers

• EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models [62.3977734456669]
  We propose Reflective Reinforcement Learning for Emotional Reasoning (EMO-R3), a framework designed to enhance the emotional reasoning ability of Multimodal Large Language Models (MLLMs)<n>We introduce Structured Emotional Thinking to guide the model to perform step-by-step emotional reasoning in a structured and interpretable manner, and design a Reflective Emotional Reward that enables the model to re-evaluate its reasoning based on visual-text consistency and emotional coherence.<n>EMO-R3 significantly improves both the interpretability and emotional intelligence of MLLMs, achieving superior performance across multiple visual emotional understanding benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T08:42:52Z)
• E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis [54.763420895859035]
  We present ELLM2-EEG-to-Emotion Large Language Model, first MLLM framework for interpretable emotion analysis from EEG.<n>ELLM integrates a pretrained EEG encoder with Q-based LLMs through learnable projection layers, employing a multi-stage training pipeline.<n>Experiments on the dataset across seven emotion categories demonstrate that ELLM2-EEG-to-Emotion Large Language Model achieves excellent performance on emotion classification.
  arXiv  Detail & Related papers  (2026-01-11T13:21:20Z)
• Emotion-Coherent Reasoning for Multimodal LLMs via Emotional Rationale Verifier [53.55996102181836]
  We propose the Emotional Rationale Verifier (ERV) and an Explanation Reward.<n>Our method guides the model to produce reasoning that is explicitly consistent with the target emotion.<n>We show that our approach not only enhances alignment between explanation and prediction but also empowers MLLMs to deliver emotionally coherent, trustworthy interactions.
  arXiv  Detail & Related papers  (2025-10-27T16:40:17Z)
• Unraveling Emotions with Pre-Trained Models [40.463050040722855]
  This work compares the effectiveness of fine-tuning and prompt engineering in emotion detection in three scenarios.<n> Experimental tests attain metrics above 70% with a fine-tuned pre-trained model for emotion recognition.<n>These advancements improve sentiment analysis, human-computer interaction, and understanding of user behavior across various domains.
  arXiv  Detail & Related papers  (2025-10-22T15:13:52Z)
• Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations [94.62792643569567]
  This work systematically investigates the role of speaker emotion.<n>We construct a dataset of malicious speech instructions expressed across multiple emotions and intensities, and evaluate several state-of-the-art LALMs.<n>Our results reveal substantial safety inconsistencies: different emotions elicit varying levels of unsafe responses, and the effect of intensity is non-monotonic, with medium expressions often posing the greatest risk.
  arXiv  Detail & Related papers  (2025-10-19T15:41:25Z)
• Customizing Visual Emotion Evaluation for MLLMs: An Open-vocabulary, Multifaceted, and Scalable Approach [29.502292089901825]
  We argue that this inconsistency stems partly from constraints in existing evaluation methods.<n>We propose an Emotion Statement Judgment task that overcomes these constraints.<n>We devise an automated pipeline that efficiently constructs emotion-centric statements with minimal human effort.
  arXiv  Detail & Related papers  (2025-09-26T06:30:39Z)
• MME-Emotion: A Holistic Evaluation Benchmark for Emotional Intelligence in Multimodal Large Language Models [108.61337743051483]
  We present MME-Emotion, a systematic benchmark that assesses both emotional understanding and reasoning capabilities of MLLMs.<n>MME-Emotion contains over 6,000 curated video clips with task-specific questioning-answering (QA) pairs, spanning broad scenarios to formulate eight emotional tasks.<n>It incorporates a holistic evaluation suite with hybrid metrics for emotion recognition and reasoning, analyzed through a multi-agent system framework.
  arXiv  Detail & Related papers  (2025-08-11T03:14:55Z)
• MLLMs are Deeply Affected by Modality Bias [158.64371871084478]
  Recent advances in Multimodal Large Language Models (MLLMs) have shown promising results in integrating diverse modalities such as texts and images.<n>MLLMs are heavily influenced by modality bias, often relying on language while under-utilizing other modalities like visual inputs.<n>This paper argues that MLLMs are deeply affected by modality bias, highlighting its manifestations across various tasks.
  arXiv  Detail & Related papers  (2025-05-24T11:49:31Z)
• AI with Emotions: Exploring Emotional Expressions in Large Language Models [0.0]
  Large Language Models (LLMs) play role-play as agents answering questions with specified emotional states. Russell's Circumplex model characterizes emotions along the sleepy-activated (arousal) and pleasure-displeasure (valence) axes. evaluation showed that the emotional states of the generated answers were consistent with the specifications.
  arXiv  Detail & Related papers  (2025-04-20T18:49:25Z)
• Sensitivity Meets Sparsity: The Impact of Extremely Sparse Parameter Patterns on Theory-of-Mind of Large Language Models [55.46269953415811]
  We identify ToM-sensitive parameters and show that perturbing as little as 0.001% of these parameters significantly degrades ToM performance. Our results have implications for enhancing model alignment, mitigating biases, and improving AI systems designed for human interaction.
  arXiv  Detail & Related papers  (2025-04-05T17:45:42Z)
• CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation [53.452699232071495]
  CrossWordBench is a benchmark designed to evaluate the reasoning capabilities of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs) through the medium of crossword puzzles. Our evaluation reveals that reasoning LLMs outperform non-reasoning models substantially by effectively leveraging crossing-letter constraints. Our findings offer insights into the limitations of the reasoning capabilities of current LLMs and LVLMs, and provide an effective approach for creating multimodal constrained tasks for future evaluations.
  arXiv  Detail & Related papers  (2025-03-30T20:03:36Z)
• From Personas to Talks: Revisiting the Impact of Personas on LLM-Synthesized Emotional Support Conversations [19.67703146838264]
  Large Language Models (LLMs) have revolutionized the generation of emotional support conversations. This paper explores the role of personas in the creation of emotional support conversations.
  arXiv  Detail & Related papers  (2025-02-17T05:24:30Z)
• Retrieving Implicit and Explicit Emotional Events Using Large Language Models [4.245183693179267]
  Large language models (LLMs) have garnered significant attention in recent years due to their impressive performance.<n>This study investigates LLMs' emotion retrieval capabilities in commonsense.
  arXiv  Detail & Related papers  (2024-10-24T19:56:28Z)
• Do Large Language Models Possess Sensitive to Sentiment? [18.88126980975737]
  Large Language Models (LLMs) have recently displayed their extraordinary capabilities in language understanding.<n>This paper investigates the ability of LLMs to detect and react to sentiment in text modal.
  arXiv  Detail & Related papers  (2024-09-04T01:40:20Z)
• EmoLLM: Multimodal Emotional Understanding Meets Large Language Models [61.179731667080326]
  Multi-modal large language models (MLLMs) have achieved remarkable performance on objective multimodal perception tasks. But their ability to interpret subjective, emotionally nuanced multimodal content remains largely unexplored. EmoLLM is a novel model for multimodal emotional understanding, incorporating with two core techniques.
  arXiv  Detail & Related papers  (2024-06-24T08:33:02Z)
• The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition [74.04775677110179]
  In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) We show that LLMs have strong yet inconsistent priors in emotion recognition that ossify their predictions. Our results suggest that caution is needed when using ICL with larger LLMs for affect-centered tasks outside their pre-training domain.
  arXiv  Detail & Related papers  (2024-03-25T19:07:32Z)
• Affect Recognition in Conversations Using Large Language Models [9.689990547610664]
  Affect recognition plays a pivotal role in human communication. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations.
  arXiv  Detail & Related papers  (2023-09-22T14:11:23Z)
• Building Emotional Support Chatbots in the Era of LLMs [64.06811786616471]
  We introduce an innovative methodology that synthesizes human insights with the computational prowess of Large Language Models (LLMs) By utilizing the in-context learning potential of ChatGPT, we generate an ExTensible Emotional Support dialogue dataset, named ExTES. Following this, we deploy advanced tuning techniques on the LLaMA model, examining the impact of diverse training strategies, ultimately yielding an LLM meticulously optimized for emotional support interactions.
  arXiv  Detail & Related papers  (2023-08-17T10:49:18Z)
• Large Language Models Understand and Can be Enhanced by Emotional Stimuli [53.53886609012119]
  We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli. Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts. Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
  arXiv  Detail & Related papers  (2023-07-14T00:57:12Z)

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.