# How Model Size, Temperature, and Prompt Style Affect LLM-Human Assessment Score Alignment

arXiv: https://arxiv.org/abs/2509.19329 · HTML: https://mlsanity.com/paper/2509.19329/

## Key facts

- Cited by 1 papers 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.

## Placed in

- LLM reasoning / [Reasoning benchmarks and evaluation](https://mlsanity.com/lr/reasoning-benchmarks-and-evaluation/)

## Builds on (most cited first, 1 of 1)

- [Can AI grade your essays? A comparative analysis of large language models and teacher ratings in multidimensional essay scoring](https://mlsanity.com/paper/2411.16337/) (2024)

## Built on by (most cited first, 1 of 1)

- [Affording Process Auditability with QualAnalyzer: An Atomistic LLM Analysis Tool for Qualitative Research](https://mlsanity.com/paper/2604.03820/) (2026)

## 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.