What's Next in Affective Modeling? Large Language Models

• URL: http://arxiv.org/abs/2310.18322v1
• Date: Tue, 3 Oct 2023 16:39:20 GMT
• Title: What's Next in Affective Modeling? Large Language Models
• Authors: Nutchanon Yongsatianchot, Tobias Thejll-Madsen, Stacy Marsella
• Abstract summary: GPT-4 performs well across multiple emotion tasks. It can distinguish emotion theories and come up with emotional stories. We suggest that LLMs could play an important role in affective modeling.
• Score: 3.0902630634005797
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLM) have recently been shown to perform well at various tasks from language understanding, reasoning, storytelling, and information search to theory of mind. In an extension of this work, we explore the ability of GPT-4 to solve tasks related to emotion prediction. GPT-4 performs well across multiple emotion tasks; it can distinguish emotion theories and come up with emotional stories. We show that by prompting GPT-4 to identify key factors of an emotional experience, it is able to manipulate the emotional intensity of its own stories. Furthermore, we explore GPT-4's ability on reverse appraisals by asking it to predict either the goal, belief, or emotion of a person using the other two. In general, GPT-4 can make the correct inferences. We suggest that LLMs could play an important role in affective modeling; however, they will not fully replace works that attempt to model the mechanisms underlying emotion-related processes.

Related papers

• From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs [12.199629860735195]
  We compare GPT4 with supervised models and or humans in three aspects: agreement with human annotations, alignment with human perception, and impact on model training. We find that common metrics that use aggregated human annotations as ground truth can underestimate the performance, of GPT-4.
  arXiv  Detail & Related papers  (2024-08-30T05:50:15Z)
• GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective [1.642094639107215]
  We first look at carefully crafted emotion-evoking stimuli, originally designed to find patterns of brain neural activity. We show that GPT-4 is especially accurate in reasoning about such stimuli. We find that GPT-4's interpretations align more closely with human judgments about the emotions of others than with self-assessments.
  arXiv  Detail & Related papers  (2024-08-11T01:22:09Z)
• 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)
• The Good, The Bad, and Why: Unveiling Emotions in Generative AI [73.94035652867618]
  We show that EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it. EmotionDecode reveals that AI models can comprehend emotional stimuli akin to the mechanism of dopamine in the human brain.
  arXiv  Detail & Related papers  (2023-12-18T11:19:45Z)
• Language Models (Mostly) Do Not Consider Emotion Triggers When Predicting Emotion [87.18073195745914]
  We investigate how well human-annotated emotion triggers correlate with features deemed salient in their prediction of emotions. Using EmoTrigger, we evaluate the ability of large language models to identify emotion triggers. Our analysis reveals that emotion triggers are largely not considered salient features for emotion prediction models, instead there is intricate interplay between various features and the task of emotion detection.
  arXiv  Detail & Related papers  (2023-11-16T06:20:13Z)
• How FaR Are Large Language Models From Agents with Theory-of-Mind? [69.41586417697732]
  We propose a new evaluation paradigm for large language models (LLMs): Thinking for Doing (T4D) T4D requires models to connect inferences about others' mental states to actions in social scenarios. We introduce a zero-shot prompting framework, Foresee and Reflect (FaR), which provides a reasoning structure that encourages LLMs to anticipate future challenges.
  arXiv  Detail & Related papers  (2023-10-04T06:47:58Z)
• 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)
• Does Conceptual Representation Require Embodiment? Insights From Large Language Models [9.390117546307042]
  We compare representations of 4,442 lexical concepts between humans and ChatGPTs (GPT-3.5 and GPT-4) We identify two main findings: 1) Both models strongly align with human representations in non-sensorimotor domains but lag in sensory and motor areas, with GPT-4 outperforming GPT-3.5; 2) GPT-4's gains are associated with its additional visual learning, which also appears to benefit related dimensions like haptics and imageability.
  arXiv  Detail & Related papers  (2023-05-30T15:06:28Z)
• Sparks of Artificial General Intelligence: Early experiments with GPT-4 [66.1188263570629]
  GPT-4, developed by OpenAI, was trained using an unprecedented scale of compute and data. We demonstrate that GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more. We believe GPT-4 could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system.
  arXiv  Detail & Related papers  (2023-03-22T16:51:28Z)

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.