arXiv AI August 14, 2026 By Jasin Cekinmez, Addison J. Wu, Thomas L. Griffiths

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

6d ago

Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4

The study investigates how Google’s Gemma 4‑e4b language model resolves conflicts between two documents. Using a counterbalanced design, researchers found that the semantic framing of a source (e.g., labeling it as an official guideline) dominates over the order in which documents appear. While the model shows a primacy bias toward the first document, this bias varies widely with wording and is amplified only when the documents are structurally identical.

By Amanda Fitch

llms safety

arXiv AI

Aug 14

Mimicry without understanding: the origins of decision bias in large language models

arXiv:2608. 12339v1 Announce Type: cross Abstract: Large Language models (LLMs) were found to be susceptible to a host of social, affective, and cognitive biases.

By Eldad Yechiam, Adi Tarabeih

llms safety

arXiv AI

Aug 7

Layer-wise Positional Bias in Short-Context Language Modeling

arXiv:2601. 04098v2 Announce Type: replace-cross Abstract: Transformer language models systematically prefer tokens at specific input positions regardless of semantic relevance---a phenomenon known as positional bias.

By Maryam Rahimi, Mahdi Nouri, Yadollah Yaghoobzadeh

llms safety

arXiv Computation and Language

Sep 1

Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments

arXiv:2608.29803v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as proxies for human participants in social simulations, yet whether they update their beliefs...

By Lin Chen, Yitong Chen, Yong Li

llms

arXiv AI

Aug 18

Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

arXiv:2608. 14681v1 Announce Type: cross Abstract: Words recur constantly in natural language use, yet it remains unclear whether language models reactivate prior representations or re-evaluate repeated words afresh, and whether post-training changes this default behavior.

By Jinglei Ren, Yuyue Wang

llms

arXiv Machine Learning

Jul 9

Dissociating the Internal Representations of Sycophancy in LLMs

arXiv:2607. 07003v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect.

By Anthony Baez, Sheer Karny, Pat Pataranutaporn

llms

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Related stories

Words Speak Louder Than Order: A Behavioral Evaluation of Gemma 4

Mimicry without understanding: the origins of decision bias in large language models

Layer-wise Positional Bias in Short-Context Language Modeling

Do LLMs Change Their Minds Like Humans? Diagnosing Human--LLM Divergence in Single-Turn Persuasion Judgments

Automatic or Controlled? Repetition Priming Reveals Divergent Processing in Base LLMs, Instruct LLMs, and Humans

Dissociating the Internal Representations of Sycophancy in LLMs