Fine-grained Affective Processing Capabilities Emerging from Large Language Models

• URL: http://arxiv.org/abs/2309.01664v1
• Date: Mon, 4 Sep 2023 15:32:47 GMT
• Title: Fine-grained Affective Processing Capabilities Emerging from Large Language Models
• Authors: Joost Broekens, Bernhard Hilpert, Suzan Verberne, Kim Baraka, Patrick Gebhard and Aske Plaat
• Abstract summary: We explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories, and c) can perform basic appraisal-based emotion elicitation of situations.
• Score: 7.17010996725842
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions, b) has meaningful emotion representations in terms of emotion categories and these affective dimensions, and c) can perform basic appraisal-based emotion elicitation of situations based on a prompt-based computational implementation of the OCC appraisal model. These findings are highly relevant: First, they show that the ability to solve complex affect processing tasks emerges from language-based token prediction trained on extensive data sets. Second, they show the potential of large language models for simulating, processing and analyzing human emotions, which has important implications for various applications such as sentiment analysis, socially interactive agents, and social robotics.

Related papers

• 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)
• Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models [44.015651538470856]
  Human emotions are often not expressed directly, but regulated according to internal processes and social display rules. No method to automatically classify different emotion regulation strategies in a cross-user scenario exists. We make use of the recently introduced textscDeep corpus for modeling the social display of the emotion shame. A fine-tuned Llama2-7B model is able to classify the utilized emotion regulation strategy with high accuracy.
  arXiv  Detail & Related papers  (2024-08-08T12:47:10Z)
• ECR-Chain: Advancing Generative Language Models to Better Emotion-Cause Reasoners through Reasoning Chains [61.50113532215864]
  Causal Emotion Entailment (CEE) aims to identify the causal utterances in a conversation that stimulate the emotions expressed in a target utterance. Current works in CEE mainly focus on modeling semantic and emotional interactions in conversations. We introduce a step-by-step reasoning method, Emotion-Cause Reasoning Chain (ECR-Chain), to infer the stimulus from the target emotional expressions in conversations.
  arXiv  Detail & Related papers  (2024-05-17T15:45:08Z)
• Contextual Emotion Recognition using Large Vision Language Models [0.6749750044497732]
  Achieving human-level recognition of the apparent emotion of a person in real world situations remains an unsolved task in computer vision. In this paper, we examine two major approaches enabled by recent large vision language models. We demonstrate that a vision language model, fine-tuned even on a small dataset, can significantly outperform traditional baselines.
  arXiv  Detail & Related papers  (2024-05-14T23:24:12Z)
• GPT as Psychologist? Preliminary Evaluations for GPT-4V on Visual Affective Computing [74.68232970965595]
  Multimodal large language models (MLLMs) are designed to process and integrate information from multiple sources, such as text, speech, images, and videos. This paper assesses the application of MLLMs with 5 crucial abilities for affective computing, spanning from visual affective tasks and reasoning tasks.
  arXiv  Detail & Related papers  (2024-03-09T13:56:25Z)
• SOUL: Towards Sentiment and Opinion Understanding of Language [96.74878032417054]
  We propose a new task called Sentiment and Opinion Understanding of Language (SOUL) SOUL aims to evaluate sentiment understanding through two subtasks: Review (RC) and Justification Generation (JG)
  arXiv  Detail & Related papers  (2023-10-27T06:48:48Z)
• Analysis of the Evolution of Advanced Transformer-Based Language Models: Experiments on Opinion Mining [0.5735035463793008]
  This paper studies the behaviour of the cutting-edge Transformer-based language models on opinion mining. Our comparative study shows leads and paves the way for production engineers regarding the approach to focus on.
  arXiv  Detail & Related papers  (2023-08-07T01:10:50Z)
• Multimodal Emotion Recognition using Transfer Learning from Speaker Recognition and BERT-based models [53.31917090073727]
  We propose a neural network-based emotion recognition framework that uses a late fusion of transfer-learned and fine-tuned models from speech and text modalities. We evaluate the effectiveness of our proposed multimodal approach on the interactive emotional dyadic motion capture dataset.
  arXiv  Detail & Related papers  (2022-02-16T00:23:42Z)
• Did the Cat Drink the Coffee? Challenging Transformers with Generalized Event Knowledge [59.22170796793179]
  Transformers Language Models (TLMs) were tested on a benchmark for the textitdynamic estimation of thematic fit Our results show that TLMs can reach performances that are comparable to those achieved by SDM. However, additional analysis consistently suggests that TLMs do not capture important aspects of event knowledge.
  arXiv  Detail & Related papers  (2021-07-22T20:52:26Z)
• Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework [83.21732533130846]
  The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2. It presents the design of two classes of deep neural networks trained with these databases. A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
  arXiv  Detail & Related papers  (2021-03-29T17:36:20Z)

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.