How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 1 of its 23 references that are in the map. First posted on arXiv 14 Sep 2025.
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