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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/2509.19329 |
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| _version_ | 1866914052650827776 |
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| author | Jung, Julie Lu, Max Benker, Sina Chole Darici, Dogus |
| author_facet | Jung, Julie Lu, Max Benker, Sina Chole Darici, Dogus |
| contents | We examined how model size, temperature, and prompt style affect Large Language Models' (LLMs) alignment within itself, between models, and with human in assessing clinical reasoning skills. Model size emerged as a key factor in LLM-human score alignment. Study highlights the importance of checking alignments across multiple levels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_19329 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment Jung, Julie Lu, Max Benker, Sina Chole Darici, Dogus Computation and Language Methodology We examined how model size, temperature, and prompt style affect Large Language Models' (LLMs) alignment within itself, between models, and with human in assessing clinical reasoning skills. Model size emerged as a key factor in LLM-human score alignment. Study highlights the importance of checking alignments across multiple levels. |
| title | How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment |
| topic | Computation and Language Methodology |
| url | https://arxiv.org/abs/2509.19329 |