arXiv AI June 15, 2026 By Achraf Cohen, Andrew Kincaid

A Two-Stage Statistical Framework for Evaluating Associative Interference in Large Language Models

arXiv:2606. 14117v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly evaluated for bias using adaptations of human psychological paradigms, yet methodological limitations-particularly the conflation of refusal behavior with task performance-have hindered clear interpretation.

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.

arXiv AI

Jun 19

Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

arXiv:2606. 20205v1 Announce Type: new Abstract: Psychological instruments designed for humans are increasingly used to assign large language models (LLMs) stable psychological profiles that affect their usability, safety assessment, and use as proxies for human participants in research.

By Jelena Meyer, David Garcia, Dirk U. Wulff

llms safety

arXiv AI

Sep 1

Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias

arXiv:2502.10577v2 Announce Type: replace-cross Abstract: Instruct-based large language models (LLMs) have been shown to propagate and even amplify gender bias when prompted with contextually constra...

By Enzo Doyen, Amalia Todirascu

llms safety

arXiv AI

Aug 13

Conflict and Congruency Effects in Large Language Models: In-Weight and In-Context Competition in a Verbal Conflict Task

arXiv:2608. 11510v1 Announce Type: cross Abstract: Congruency effects, observed in conflict tasks such as Stroop and flanker tasks, have been investigated for nearly a century in psychology and neuroscience, but their mechanistic basis is not fully understood.

By Xiaoyang Hu, Mike Angstadt, Shane Storks, Zan Huang, Aman Taxali, Alex Weigard, Richard L. Lewis, Chandra Sripada

llms fine-tuning

arXiv Machine Learning

Sep 21

Causal Evidence that Language Models use Confidence to Drive Behavior

arXiv:2603.22161v3 Announce Type: replace Abstract: Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstr...

By Dharshan Kumaran, Nathaniel Daw, Simon Osindero, Petar Veli\v{c}kovi\'c, Viorica Patraucean

llms agents

arXiv AI

Aug 3

The Asymmetric Effects of Knowledge Distillation on Bias in Small Language Models

arXiv:2607. 28639v1 Announce Type: cross Abstract: We show that knowledge distillation in small instruction-tuned language models has asymmetric effects on bias.

By Plawan Kumar Rath

llms fine-tuning efficiency safety

arXiv AI

Sep 10

Linear Separability of Activation Representations after Supervised Fine-Tuning on Incorrect Responses: A Study of Synthetic Dishonesty in Large Language Models

arXiv:2605.30381v2 Announce Type: replace-cross Abstract: When a language model is fine-tuned to produce systematically incorrect responses, does this training leave a structured, linearly recoverabl...

By Vahideh Zolfaghari

llms fine-tuning benchmarks

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Related stories

Apparent Psychological Profiles of Large Language Models are Largely a Measurement Artifact

Man Made Language Models? Evaluating LLMs' Perpetuation of Masculine Generics Bias

Conflict and Congruency Effects in Large Language Models: In-Weight and In-Context Competition in a Verbal Conflict Task

Causal Evidence that Language Models use Confidence to Drive Behavior

The Asymmetric Effects of Knowledge Distillation on Bias in Small Language Models

Linear Separability of Activation Representations after Supervised Fine-Tuning on Incorrect Responses: A Study of Synthetic Dishonesty in Large Language Models