Affect Recognition in Conversations Using Large Language Models

• URL: http://arxiv.org/abs/2309.12881v2
• Date: Mon, 5 Aug 2024 12:13:39 GMT
• Title: Affect Recognition in Conversations Using Large Language Models
• Authors: Shutong Feng, Guangzhi Sun, Nurul Lubis, Wen Wu, Chao Zhang, Milica Gašić,
• Abstract summary: 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.
• Score: 9.689990547610664
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Affect recognition, encompassing emotions, moods, and feelings, plays a pivotal role in human communication. In the realm of conversational artificial intelligence, the ability to discern and respond to human affective cues is a critical factor for creating engaging and empathetic interactions. This study investigates the capacity of large language models (LLMs) to recognise human affect in conversations, with a focus on both open-domain chit-chat dialogues and task-oriented dialogues. Leveraging three diverse datasets, namely IEMOCAP (Busso et al., 2008), EmoWOZ (Feng et al., 2022), and DAIC-WOZ (Gratch et al., 2014), covering a spectrum of dialogues from casual conversations to clinical interviews, we evaluate and compare LLMs' performance in affect recognition. Our investigation explores the zero-shot and few-shot capabilities of LLMs through in-context learning as well as their model capacities through task-specific fine-tuning. Additionally, this study takes into account the potential impact of automatic speech recognition errors on LLM predictions. With this work, we aim to shed light on the extent to which LLMs can replicate human-like affect recognition capabilities in conversations.

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)
• LMLPA: Language Model Linguistic Personality Assessment [11.599282127259736]
  Large Language Models (LLMs) are increasingly used in everyday life and research. measuring the personality of a given LLM is currently a challenge. This paper introduces the Language Model Linguistic Personality Assessment (LMLPA), a system designed to evaluate the linguistic personalities of LLMs.
  arXiv  Detail & Related papers  (2024-10-23T07:48:51Z)
• AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models [18.482881562645264]
  This study is the first to explore the potential of Large Language Models (LLMs) in recognizing ambiguous emotions. We design zero-shot and few-shot prompting and incorporate past dialogue as context information for ambiguous emotion recognition.
  arXiv  Detail & Related papers  (2024-09-26T23:25:21Z)
• Cross-lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models [16.0617753653454]
  This study presents a comparative analysis between human performance and SSL models. We also compare the SER ability of models and humans at both utterance- and segment-levels. Our findings reveal that models, with appropriate knowledge transfer, can adapt to the target language and achieve performance comparable to native speakers.
  arXiv  Detail & Related papers  (2024-09-25T13:27:17Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues [53.52699766206808]
  We present a first attempt at performing knowledge tracing (KT) in tutor-student dialogues. We propose methods to identify the knowledge components/skills involved in each dialogue turn. We then apply a range of KT methods on the resulting labeled data to track student knowledge levels over an entire dialogue.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance [73.19687314438133]
  We study how reliance is affected by contextual features of an interaction. We find that contextual characteristics significantly affect human reliance behavior. Our results show that calibration and language quality alone are insufficient in evaluating the risks of human-LM interactions.
  arXiv  Detail & Related papers  (2024-07-10T18:00:05Z)
• Can LLMs Understand the Implication of Emphasized Sentences in Dialogue? [64.72966061510375]
  Emphasis is a crucial component in human communication, which indicates the speaker's intention and implication beyond pure text in dialogue. This paper introduces Emphasized-Talk, a benchmark with emphasis-annotated dialogue samples capturing the implications of emphasis. We evaluate various Large Language Models (LLMs), both open-source and commercial, to measure their performance in understanding emphasis.
  arXiv  Detail & Related papers  (2024-06-16T20:41:44Z)
• Affective-NLI: Towards Accurate and Interpretable Personality Recognition in Conversation [30.820334868031537]
  Personality Recognition in Conversation (PRC) aims to identify the personality traits of speakers through textual dialogue content. We propose Affective Natural Language Inference (Affective-NLI) for accurate and interpretable PRC.
  arXiv  Detail & Related papers  (2024-04-03T09:14:24Z)
• Reasoning in Conversation: Solving Subjective Tasks through Dialogue Simulation for Large Language Models [56.93074140619464]
  We propose RiC (Reasoning in Conversation), a method that focuses on solving subjective tasks through dialogue simulation. The motivation of RiC is to mine useful contextual information by simulating dialogues instead of supplying chain-of-thought style rationales. We evaluate both API-based and open-source LLMs including GPT-4, ChatGPT, and OpenChat across twelve tasks.
  arXiv  Detail & Related papers  (2024-02-27T05:37:10Z)
• LLM Agents in Interaction: Measuring Personality Consistency and Linguistic Alignment in Interacting Populations of Large Language Models [4.706971067968811]
  We create a two-group population of large language models (LLMs) agents using a simple variability-inducing sampling algorithm. We administer personality tests and submit the agents to a collaborative writing task, finding that different profiles exhibit different degrees of personality consistency and linguistic alignment to their conversational partners.
  arXiv  Detail & Related papers  (2024-02-05T11:05:20Z)
• 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.