Human-like moral judgments conceal divergent motive attributions in large language models
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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: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.
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.