Hugging Face Trending Papers June 4, 2026

Analysis of the Neglect-Zero Effect in Large Language Models

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We investigate the extent to which the language processing of LLMs resembles human cognitive processes, focusing on a human cognitive bias called the $\textit{neglect-zero effect}$. This effect refers to the human tendency to ignore $\textit{zero-models}$, which are configurations that render a proposition vacuously true by virtue of an empty set.

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arXiv Machine Learning

Aug 6

When More Becomes Less: Position-Dependent Repetition Effects in Language Models

arXiv:2608. 04021v1 Announce Type: cross Abstract: Cloze-style probes that vary how often a target token appears implicitly assume that more copies of a target affect prediction the same way regardless of where the readout slot sits.

By Han-yu Wang

llms benchmarks

arXiv AI

1d ago

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

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By Jinglei Ren, Yuyue Wang

llms

arXiv AI

5d ago

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.

By Jasin Cekinmez, Addison J. Wu, Thomas L. Griffiths

llms

arXiv AI

Jun 2

Relational Intervention During Functional Collapse in Large Language Models: A Lexical-Statistical Ablation and a Structure x Register Factorial

arXiv:2606. 00935v1 Announce Type: new Abstract: We test whether a relational-style intervention delivered during functional collapse in a small language model produces post-collapse behavior distinguishable from technical feedback, from a lexically-matched scrambled control, and from each of the two pragmatic dimensions in isolation.

By Franco Santana, Horacio Vico

llms benchmarks

arXiv AI

6d ago

Social Meaning in Large Language Models: Structure, Magnitude, and Pragmatic Prompting

arXiv:2604. 02512v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly exhibit human-like patterns of pragmatic and social reasoning.

By Roland M\"uhlenbernd

llms

arXiv AI

Jul 17

Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect

arXiv:2607. 14111v1 Announce Type: cross Abstract: Can small language models detect and report on perturbations their own internal activations?

By Ely Hahami, Ishaan Sinha, Lavik Jain

llms fine-tuning benchmarks safety

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When More Becomes Less: Position-Dependent Repetition Effects in Language Models

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

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Introspection Fine-Tuning (IFT): Training Small LLMs to Introspect