The paper investigates why large language models (LLMs) accept or reject generic statements, drawing on psycholinguistic research that identifies factors beyond mere property frequency, such as distinctiveness and characteristicness. Using twelve LLMs evaluated on a psycholinguistic dataset and real-world generics, the study finds that models weigh property commonality against distinctiveness similarly to humans, but they prioritize factual truth and largely ignore striking properties. Manipulating hidden states along factor directions confirms that each factor influences judgments as predicted.
By Mervat Abassy, Zhaoxin Feng, Tatsuki Kuribayashi, Kentaro Inui, Emmanuele Chersoni
Large Language Models (LLMs) frequently exhibit sycophancy, where they agree with a user's statement even when incorrect. While sycophancy is often treated as a single defined behavior, it can manifest in substantially distinct ways and circumstances, raising the question of whether this multi-faceted nature is reflected in its internal mechanisms.
arXiv:2610.28066v1 Announce Type: cross
Abstract: Multilingual evaluations often score a model's opening yes or no, a token that also carries model and language answering conventions. We ask when dif...
By Haonan Huang, Yuwei Lu, Chengkun Cai
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
arXiv:2609.07943v1 Announce Type: new
Abstract: There is significant uncertainty about whether abstractions like beliefs or desires usefully describe the behavior of large language models (LLMs). In...
By Alex Smolin, Bryan Wilder
arXiv:2603.18007v2 Announce Type: replace-cross
Abstract: The study explores whether current Large Language Models (LLMs) exhibit Theory of Mind (ToM) capabilities -- specifically, the ability to inf...
By Anna Babarczy, Andras Lukacs, Peter Vedres, Zeteny Bujka
The paper studies how large language models (LLMs) handle the discourse particles "just" and "only" in natural conversation. Behavioral experiments show that both humans and LLMs prefer these particles and are more sensitive to "just" than "only", while instruction tuning enhances this sensitivity. Mechanistic analysis identifies specific units in the models that respond to each particle, revealing an asymmetric overlap in their internal representations.
By William Sheffield, Jiahui Chen, Amy Zhang, Alex Warstadt, Junyi Jessy Li, Kanishka Misra
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
arXiv:2601.09041v2 Announce Type: replace-cross
Abstract: Large language models (LLMs) often produce judgments similar to those of humans, but their interpretation of figurative language remains uncl...
By Samhita Bollepally, Aurora Sloman-Moll, Takashi Yamauchi, Neeka Motamedrasa
The study investigates whether large language models (LLMs) are more prone to errors when they doubt the plausibility of input data, a phenomenon termed context‑memory conflict. Using non‑English and low‑resource language datasets, the authors generate text from factual, counterfactual, and fictional RDF triples in English, Czech, Slovak, and Upper Sorbian, and evaluate faithfulness with both human annotations and an LLM judge (Kimi K3). Contrary to expectations, the results show only a weak context‑memory conflict: counterfactual inputs receive slightly lower faithfulness scores than factual ones, and the choice of LLM judge can significantly affect perceived conflict strength.
By Peter Kochelka, Ale\v{s} Manuel Pap\'a\v{c}ek, Vojt\v{e}ch Dvo\v{r}\'ak, Ond\v{r}ej Du\v{s}ek
arXiv:2607. 27824v1 Announce Type: cross Abstract: LLMs encode, convey, and perpetuate stereotypes.
By Farane Jalali Farahani, Corina Dima, Mojtaba Nayyeri, Raphael H. Heiberger, Steffen Staab
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