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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.23875 |
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| _version_ | 1866915581962223616 |
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| author | Jayakumar, Eswari Dash, Niladri Sekhar Mukherjee, Debasmita |
| author_facet | Jayakumar, Eswari Dash, Niladri Sekhar Mukherjee, Debasmita |
| contents | While Large Language Model (LLM)-based agents can be used to create highly engaging interactive applications through prompting personality traits and contextual data, effectively assessing their personalities has proven challenging. This novel interdisciplinary approach addresses this gap by combining agent development and linguistic analysis to assess the prompted personality of LLM-based agents in a poetry explanation task. We developed a novel, flexible question bank, informed by linguistic assessment criteria and human cognitive learning levels, offering a more comprehensive evaluation than current methods. By evaluating agent responses with natural language processing models, other LLMs, and human experts, our findings illustrate the limitations of purely deep learning solutions and emphasize the critical role of interdisciplinary design in agent development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23875 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Large Language Model Agent Personality and Response Appropriateness: Evaluation by Human Linguistic Experts, LLM-as-Judge, and Natural Language Processing Model Jayakumar, Eswari Dash, Niladri Sekhar Mukherjee, Debasmita Human-Computer Interaction While Large Language Model (LLM)-based agents can be used to create highly engaging interactive applications through prompting personality traits and contextual data, effectively assessing their personalities has proven challenging. This novel interdisciplinary approach addresses this gap by combining agent development and linguistic analysis to assess the prompted personality of LLM-based agents in a poetry explanation task. We developed a novel, flexible question bank, informed by linguistic assessment criteria and human cognitive learning levels, offering a more comprehensive evaluation than current methods. By evaluating agent responses with natural language processing models, other LLMs, and human experts, our findings illustrate the limitations of purely deep learning solutions and emphasize the critical role of interdisciplinary design in agent development. |
| title | Large Language Model Agent Personality and Response Appropriateness: Evaluation by Human Linguistic Experts, LLM-as-Judge, and Natural Language Processing Model |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.23875 |