Pith. sign in

REVIEW 1 cited by

The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skills

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2409.14050 v1 pith:YTSS7GAJ submitted 2024-09-21 cs.AI

classification cs.AI
keywords communicationllmsinterpersonalscalesskillsgpt-4oimprovementmodels
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

OpenAI's ChatGPT (GPT-4 and GPT-4o) and other Large Language Models (LLMs) like Microsoft's Copilot, Google's Gemini 1.5 Pro, and Antrophic's Claude 3.5 Sonnet can be effectively used in various phases of scientific research. Their performance in diverse verbal tasks and reasoning is close to or above the average human level and rapidly increasing, providing those models with a capacity that resembles a relatively high level of theory of mind. The current ability of LLMs to process information about human psychology and communication creates an opportunity for their scientific use in the fields of personality psychology and interpersonal communication skills. This article illustrates the possible uses of GPT-4o and other advanced LLMs for typical tasks in designing self-assessment scales for interpersonal communication skills measurement like the selection and improvement of scale items and evaluation of content validity of scales. The potential for automated item generation and application is illustrated as well. The case study examples are accompanied by prompts for LLMs that can be useful for these purposes. Finally, a summary is provided of the potential benefits of using LLMs in the process of evaluation, design, and improvement of interpersonal communication skills self-assessment scales.

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. EmoPerso: Enhancing Personality Detection with Self-Supervised Emotion-Aware Modelling

    cs.CL 2025-09 conditional novelty 5.0 of 10

    EmoPerso improves MBTI personality detection by training an emotion head on heuristic pseudo-labels and using cross-attention with reasoning chains, achieving 81.07% Macro-F1 on Kaggle and 68.60% on Pandora.

Pith tools