Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
- URL: http://arxiv.org/abs/2507.02537v1
- Date: Thu, 03 Jul 2025 11:32:41 GMT
- Title: Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
- Authors: Paulo Ricardo Knob, Leonardo Scholler, Juliano Rigatti, Soraia Raupp Musse,
- Abstract summary: We explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions.<n>We analyzed the emotional progression of the dialogues using both sentiment analysis (via VADER) and expert assessments.
- Score: 0.5849783371898033
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Conversational agents have made significant progress since ELIZA, expanding their role across various domains, including healthcare, education, and customer service. As these agents become increasingly integrated into daily human interactions, the need for emotional intelligence, particularly empathetic listening, becomes increasingly essential. In this study, we explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions. Starting from a small dataset manually crafted by an expert to reflect empathic behavior, we extended the conversations using two LLMs: ChatGPT and Gemini. We analyzed the emotional progression of the dialogues using both sentiment analysis (via VADER) and expert assessments. While the generated conversations often mirrored the intended emotional structure, human evaluation revealed important differences in the perceived empathy and coherence of the responses. These findings suggest that emotion modeling in dialogues requires not only structural alignment in the expressed emotions but also qualitative depth, highlighting the importance of combining automated and humancentered methods in the development of emotionally competent agents.
Related papers
- Interactive Dialogue Agents via Reinforcement Learning on Hindsight Regenerations [58.65755268815283]
Many real dialogues are interactive, meaning an agent's utterances will influence their conversational partner, elicit information, or change their opinion.
We use this fact to rewrite and augment existing suboptimal data, and train via offline reinforcement learning (RL) an agent that outperforms both prompting and learning from unaltered human demonstrations.
Our results in a user study with real humans show that our approach greatly outperforms existing state-of-the-art dialogue agents.
arXiv Detail & Related papers (2024-11-07T21:37:51Z) - CAPE: A Chinese Dataset for Appraisal-based Emotional Generation using Large Language Models [30.40159858361768]
We introduce a two-stage automatic data generation framework to create CAPE, a Chinese dataset named Cognitive Appraisal theory-based Emotional corpus.
This corpus facilitates the generation of dialogues with contextually appropriate emotional responses by accounting for diverse personal and situational factors.
Our study shows the potential for advancing emotional expression in conversational agents, paving the way for more nuanced and meaningful human-computer interactions.
arXiv Detail & Related papers (2024-10-18T03:33:18Z) - Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN)
EDEN recognizes emotion and causes in an explicitly thinking way.
It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
arXiv Detail & Related papers (2024-06-07T08:58:29Z) - SemEval-2024 Task 3: Multimodal Emotion Cause Analysis in Conversations [53.60993109543582]
SemEval-2024 Task 3, named Multimodal Emotion Cause Analysis in Conversations, aims at extracting all pairs of emotions and their corresponding causes from conversations.
Under different modality settings, it consists of two subtasks: Textual Emotion-Cause Pair Extraction in Conversations (TECPE) and Multimodal Emotion-Cause Pair Extraction in Conversations (MECPE)
In this paper, we introduce the task, dataset and evaluation settings, summarize the systems of the top teams, and discuss the findings of the participants.
arXiv Detail & Related papers (2024-05-19T09:59:00Z) - Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system.
We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context.
Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
arXiv Detail & Related papers (2024-04-03T08:48:50Z) - CAB: Empathetic Dialogue Generation with Cognition, Affection and
Behavior [8.791757758576951]
We propose a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses.
For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge.
For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors' emotions.
arXiv Detail & Related papers (2023-02-03T14:31:17Z) - EmoWOZ: A Large-Scale Corpus and Labelling Scheme for Emotion in
Task-Oriented Dialogue Systems [3.3010169113961325]
EmoWOZ is a large-scale manually emotion-annotated corpus of task-oriented dialogues.
It contains more than 11K dialogues with more than 83K emotion annotations of user utterances.
We propose a novel emotion labelling scheme, which is tailored to task-oriented dialogues.
arXiv Detail & Related papers (2021-09-10T15:00:01Z) - Towards Emotion-Aware Agents For Negotiation Dialogues [2.1454205511807234]
Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making.
Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI.
We analyze the extent to which emotion attributes extracted from the negotiation help in the prediction.
arXiv Detail & Related papers (2021-07-28T04:42:36Z) - Emotion-aware Chat Machine: Automatic Emotional Response Generation for
Human-like Emotional Interaction [55.47134146639492]
This article proposes a unifed end-to-end neural architecture, which is capable of simultaneously encoding the semantics and the emotions in a post.
Experiments on real-world data demonstrate that the proposed method outperforms the state-of-the-art methods in terms of both content coherence and emotion appropriateness.
arXiv Detail & Related papers (2021-06-06T06:26:15Z) - Knowledge Bridging for Empathetic Dialogue Generation [52.39868458154947]
Lack of external knowledge makes empathetic dialogue systems difficult to perceive implicit emotions and learn emotional interactions from limited dialogue history.
We propose to leverage external knowledge, including commonsense knowledge and emotional lexical knowledge, to explicitly understand and express emotions in empathetic dialogue generation.
arXiv Detail & Related papers (2020-09-21T09:21:52Z) - Generating Emotionally Aligned Responses in Dialogues using Affect
Control Theory [15.848210524718219]
Affect Control Theory (ACT) is a socio-mathematical model of emotions for human-human interactions.
We investigate how ACT can be used to develop affect-aware neural conversational agents.
arXiv Detail & Related papers (2020-03-07T19:31:08Z)
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.