Is GPT a Computational Model of Emotion? Detailed Analysis

• URL: http://arxiv.org/abs/2307.13779v1
• Date: Tue, 25 Jul 2023 19:34:44 GMT
• Title: Is GPT a Computational Model of Emotion? Detailed Analysis
• Authors: Ala N. Tak and Jonathan Gratch
• Abstract summary: This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. It shows that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses.
• Score: 2.0001091112545066
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper investigates the emotional reasoning abilities of the GPT family of large language models via a component perspective. The paper first examines how the model reasons about autobiographical memories. Second, it systematically varies aspects of situations to impact emotion intensity and coping tendencies. Even without the use of prompt engineering, it is shown that GPT's predictions align significantly with human-provided appraisals and emotional labels. However, GPT faces difficulties predicting emotion intensity and coping responses. GPT-4 showed the highest performance in the initial study but fell short in the second, despite providing superior results after minor prompt engineering. This assessment brings up questions on how to effectively employ the strong points and address the weak areas of these models, particularly concerning response variability. These studies underscore the merits of evaluating models from a componential perspective.

Related papers

• Assessing instructor-AI cooperation for grading essay-type questions in an introductory sociology course [0.0]
  We evaluate generative pre-trained transformers (GPT) models' performance in transcribing and scoring students' responses. For grading, GPT demonstrated strong correlations with the human grader scores, especially when template answers were provided. This study contributes to the growing literature on AI in education, demonstrating its potential to enhance fairness and efficiency in grading essay-type questions.
  arXiv  Detail & Related papers  (2025-01-11T07:18:12Z)
• 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)
• What's Next in Affective Modeling? Large Language Models [3.0902630634005797]
  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.
  arXiv  Detail & Related papers  (2023-10-03T16:39:20Z)
• Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting [64.80538055623842]
  sociodemographic prompting is a technique that steers the output of prompt-based models towards answers that humans with specific sociodemographic profiles would give. We show that sociodemographic information affects model predictions and can be beneficial for improving zero-shot learning in subjective NLP tasks.
  arXiv  Detail & Related papers  (2023-09-13T15:42:06Z)
• Fine-grained Affective Processing Capabilities Emerging from Large Language Models [7.17010996725842]
  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.
  arXiv  Detail & Related papers  (2023-09-04T15:32:47Z)
• A Wide Evaluation of ChatGPT on Affective Computing Tasks [32.557383931586266]
  We study the capabilities of the ChatGPT models, namely GPT-4 and GPT-3.5, on 13 affective computing problems. We compare ChatGPT against more traditional NLP methods, such as end-to-end recurrent neural networks and transformers. The results demonstrate the emergent abilities of the ChatGPT models on a wide range of affective computing problems.
  arXiv  Detail & Related papers  (2023-08-26T16:10:30Z)
• Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias [57.42417061979399]
  Recent studies show that instruction tuning (IT) and reinforcement learning from human feedback (RLHF) improve the abilities of large language models (LMs) dramatically. In this work, we investigate the effect of IT and RLHF on decision making and reasoning in LMs. Our findings highlight the presence of these biases in various models from the GPT-3, Mistral, and T5 families.
  arXiv  Detail & Related papers  (2023-08-01T01:39:25Z)
• SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning [5.177947445379688]
  This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations.
  arXiv  Detail & Related papers  (2023-07-16T05:33:35Z)
• Collaborative Generative AI: Integrating GPT-k for Efficient Editing in Text-to-Image Generation [114.80518907146792]
  We investigate the potential of utilizing large-scale language models, such as GPT-k, to improve the prompt editing process for text-to-image generation. We compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process.
  arXiv  Detail & Related papers  (2023-05-18T21:53:58Z)
• A Causal Framework to Quantify the Robustness of Mathematical Reasoning with Language Models [81.15974174627785]
  We study the behavior of language models in terms of robustness and sensitivity to direct interventions in the input space. Our analysis shows that robustness does not appear to continuously improve as a function of size, but the GPT-3 Davinci models (175B) achieve a dramatic improvement in both robustness and sensitivity compared to all other GPT variants.
  arXiv  Detail & Related papers  (2022-10-21T15:12:37Z)

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.