Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings

• URL: http://arxiv.org/abs/2503.16883v2
• Date: Mon, 24 Mar 2025 14:38:36 GMT
• Title: Assessing the Reliability and Validity of GPT-4 in Annotating Emotion Appraisal Ratings
• Authors: Deniss Ruder, Andero Uusberg, Kairit Sirts,
• Abstract summary: This paper studies GPT-4 as a reader-annotator of 21 specific appraisal ratings in different prompt settings.<n>We found that GPT-4 is an effective reader-annotator that performs close to or even slightly better than human annotators.
• Score: 0.6008132390640295
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Appraisal theories suggest that emotions arise from subjective evaluations of events, referred to as appraisals. The taxonomy of appraisals is quite diverse, and they are usually given ratings on a Likert scale to be annotated in an experiencer-annotator or reader-annotator paradigm. This paper studies GPT-4 as a reader-annotator of 21 specific appraisal ratings in different prompt settings, aiming to evaluate and improve its performance compared to human annotators. We found that GPT-4 is an effective reader-annotator that performs close to or even slightly better than human annotators, and its results can be significantly improved by using a majority voting of five completions. GPT-4 also effectively predicts appraisal ratings and emotion labels using a single prompt, but adding instruction complexity results in poorer performance. We also found that longer event descriptions lead to more accurate annotations for both model and human annotator ratings. This work contributes to the growing usage of LLMs in psychology and the strategies for improving GPT-4 performance in annotating appraisals.

Related papers

• Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams [48.99818550820575]
  We leverage state-of-the-art multi-modal AI models, in particular GPT-4o, to automatically grade handwritten responses to college-level math exams. Using real student responses to questions in a probability theory exam, we evaluate GPT-4o's alignment with ground-truth scores from human graders using various prompting techniques.
  arXiv  Detail & Related papers  (2024-11-07T22:51:47Z)
• How Effectively Do LLMs Extract Feature-Sentiment Pairs from App Reviews? [2.218667838700643]
  This study compares the performance of state-of-the-art LLMs, including GPT-4, ChatGPT, and different variants of Llama-2 chat.<n>For predicting positive and neutral sentiments, GPT-4 achieves f1-scores of 76% and 45% in the zero-shot setting.
  arXiv  Detail & Related papers  (2024-09-11T10:21:13Z)
• GroUSE: A Benchmark to Evaluate Evaluators in Grounded Question Answering [0.0]
  Retrieval-Augmented Generation (RAG) has emerged as a common paradigm to use Large Language Models (LLMs) alongside private and up-to-date knowledge bases. We address the challenges of using LLM-as-a-Judge when evaluating grounded answers generated by RAG systems.
  arXiv  Detail & Related papers  (2024-09-10T15:39:32Z)
• 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)
• Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models [92.66784679667441]
  Prometheus 2 is a more powerful evaluator LM that closely mirrors human and GPT-4 judgements. It is capable of processing both direct assessment and pairwise ranking formats grouped with a user-defined evaluation criteria. On four direct assessment benchmarks and four pairwise ranking benchmarks, Prometheus 2 scores the highest correlation and agreement with humans and proprietary LM judges.
  arXiv  Detail & Related papers  (2024-05-02T17:59:35Z)
• An In-depth Evaluation of GPT-4 in Sentence Simplification with Error-based Human Assessment [10.816677544269782]
  We design an error-based human annotation framework to assess the GPT-4's simplification capabilities. Results show that GPT-4 generally generates fewer erroneous simplification outputs compared to the current state-of-the-art.
  arXiv  Detail & Related papers  (2024-03-08T00:19:24Z)
• CritiqueLLM: Towards an Informative Critique Generation Model for Evaluation of Large Language Model Generation [87.44350003888646]
  Eval-Instruct can acquire pointwise grading critiques with pseudo references and revise these critiques via multi-path prompting. CritiqueLLM is empirically shown to outperform ChatGPT and all the open-source baselines.
  arXiv  Detail & Related papers  (2023-11-30T16:52:42Z)
• Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks [9.801767683867125]
  We provide a preliminary and hybrid evaluation on three NLP benchmarks using both automatic and human evaluation. We find that ChatGPT consistently outperforms many other popular models according to human reviewers on the majority of metrics. We also find that human reviewers rate the gold reference as much worse than the best models' outputs, indicating the poor quality of many popular benchmarks.
  arXiv  Detail & Related papers  (2023-10-20T20:17:09Z)
• Is GPT-4 a reliable rater? Evaluating Consistency in GPT-4 Text Ratings [63.35165397320137]
  This study investigates the consistency of feedback ratings generated by OpenAI's GPT-4. The model rated responses to tasks within the Higher Education subject domain of macroeconomics in terms of their content and style.
  arXiv  Detail & Related papers  (2023-08-03T12:47:17Z)
• ARB: Advanced Reasoning Benchmark for Large Language Models [94.37521840642141]
  We introduce ARB, a novel benchmark composed of advanced reasoning problems in multiple fields. As a subset of ARB, we introduce a challenging set of math and physics problems which require advanced symbolic reasoning and domain knowledge. We evaluate recent models such as GPT-4 and Claude on ARB and demonstrate that current models score well below 50% on more demanding tasks.
  arXiv  Detail & Related papers  (2023-07-25T17:55:19Z)
• Comparative Analysis of GPT-4 and Human Graders in Evaluating Praise Given to Students in Synthetic Dialogues [2.3361634876233817]
  Large language models, such as the AI-chatbot ChatGPT, hold potential for offering constructive feedback to tutors in practical settings. The accuracy of AI-generated feedback remains uncertain, with scant research investigating the ability of models like ChatGPT to deliver effective feedback.
  arXiv  Detail & Related papers  (2023-07-05T04:14:01Z)
• Can GPT-4 Support Analysis of Textual Data in Tasks Requiring Highly Specialized Domain Expertise? [0.8924669503280334]
  GPT-4, prompted with annotation guidelines, performs on par with well-trained law student annotators. We demonstrated how to analyze GPT-4's predictions to identify and mitigate deficiencies in annotation guidelines.
  arXiv  Detail & Related papers  (2023-06-24T08:48:24Z)
• GPT-4 Technical Report [116.90398195245983]
  GPT-4 is a large-scale, multimodal model which can accept image and text inputs and produce text outputs. It exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers.
  arXiv  Detail & Related papers  (2023-03-15T17:15:04Z)

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.