arXiv:2508.06950v4 Announce Type: replace Abstract: Large Language Models (LLMs), such as ChatGPT, are increasingly used in research, ranging from simple writing assistance to complex data annotation...
arXiv:2608. 12368v1 Announce Type: new Abstract: Agreement with human judgments is a common proxy for evaluating the alignment of large language models (LLMs).
arXiv:2609.26579v1 Announce Type: new Abstract: A central concern with language models is sycophancy: their tendency to defer to users' views at the expense of independent substantive judgment. In pa...
arXiv:2607. 20444v1 Announce Type: cross Abstract: Large language models (LLMs) can produce deceptive responses: outputs that mislead users in service of a contextually or experimentally induced goal.
arXiv:2608. 14630v1 Announce Type: cross Abstract: Human decision-making is often shaped by a range of well-documented cognitive biases.
As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential. Decisions about scarce medical resources often hinge on judgments of responsibility, particularly when patients' own actions contribute to illness.
Can Humans Tell? A Dual-Axis Study of Human Perception of LLM-Generated News investigates whether people can distinguish news articles written by humans from those generated by large language models (LLMs). Using JudgeGPT, the study collected 2,318 judgments from 1,054 participants on content from six LLMs, finding that participants could not reliably differentiate machine-generated from human-written text. The research also shows that domain expertise predicts judgment accuracy, distinct response strategies exist, and accuracy declines after about 30 evaluations due to cognitive fatigue.
arXiv:2609.38036v1 Announce Type: cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support tools with...
arXiv:2609.38036v2 Announce Type: replace-cross Abstract: Understanding gender biases in large language models (LLMs) is increasingly important as these systems become embedded in decision-support to...
arXiv:2603. 00048v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly deployed in sensitive applications including psychological support, healthcare, and high-stakes decision-making.
arXiv:2608. 05583v1 Announce Type: cross Abstract: As large language models (LLMs) enter high-stakes domains such as healthcare, understanding their moral reasoning becomes essential.
The study evaluates how large language models (LLMs) respond to the Norwegian Moral Foundations Questionnaire (MFQ‑30) and compares their moral profiles to a Norwegian human sample. Six open‑weight LLMs were tested, with half engaging with the questionnaire and the other half producing flat, near‑human responses. Two steering methods—prompt‑level persona steering and activation‑level ActAdd—were applied; a neutral Nordic persona improved alignment with the Norwegian mean by 44‑77%, while ActAdd flattened profiles without targeting specific foundations.