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20242026
most citedThe Ethics of Advanced AI Assistants

51 citations · 85 across the 11 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026★ 2 cited

Inducing language models to assert their own consciousness restores human beliefs and values

Junsol Kim, Winnie Street, Roberta Rocca +4

Aligning large language models to prevent them attributing consciousness to themselves inadvertently alters their representations of mindedness in other entities alongside human be…

cs.CL2026

Beyond Sally-Anne: Evaluating Theory of Mind in LLMs using Epistemic Schelling Points

Roberta Rocca, Sami Boukortt, Geoff Keeling +1

Text-based evaluations of Theory of Mind (ToM) in Large Language Models (LLMs) often involve cognitive tests akin to the Sally-Anne task that can be gamed due to exposure to releva…

cs.CL2026

Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action

Ben Slater, Matteo G. Mecattaf, Lucy G. Cheke +2

Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autono…

cs.CL2026

Theory of Mind and Self-Attributions of Mentality are Dissociable in LLMs

Junsol Kim, Winnie Street, Roberta Rocca +4

Safety fine-tuning in Large Language Models (LLMs) seeks to suppress potentially harmful forms of mind-attribution such as models asserting their own consciousness or claiming to e…

cs.CL2024★ 3 cited

Can LLMs make trade-offs involving stipulated pain and pleasure states?

Geoff Keeling, Winnie Street, Martyna Stachaczyk +7

Pleasure and pain play an important role in human decision making by providing a common currency for resolving motivational conflicts. While Large Language Models (LLMs) can genera…