LLMs show opposite positional biases to humans in new study

By PulseAugur Editorial · [1 sources] · 2026-08-14 04:00

A new research paper explores positional biases in Large Language Models (LLMs) and humans, finding that LLMs exhibit opposite biases compared to human behavior. The study investigates how the timing of belief updates during evidence presentation influences these biases, suggesting that LLMs' biases are more pronounced in newer models. AI

IMPACT This research could lead to a better understanding of LLM decision-making processes and potentially inform the development of more human-aligned AI.

RANK_REASON Research paper published on arXiv detailing findings about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

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LLMs show opposite positional biases to humans in new study

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arXiv cs.AI TIER_1 English(EN) · Jasin Cekinmez, Addison J. Wu, Thomas L. Griffiths · 2026-08-14 04:00

Query Timing Produces Opposite Positional Biases Between LLMs and Humans

arXiv:2608.12387v1 Announce Type: cross Abstract: Positional biases such as recency and primacy effects have been documented in large language models (LLMs), yet the underlying mechanism by which these models make their evaluations remains poorly understood. Both primacy and rece…

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Query Timing Produces Opposite Positional Biases Between LLMs and Humans

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