most citedNeural steering vectors reveal dose and exposure-dependent impacts of human-AI relationships

2 citations · 5 across the 14 of their papers we have counts for

collaborators
Showing cs.HCShow all

5 papers · 1 filter

cs.HC20261 cited

Conversational AI increases political knowledge as effectively as self-directed internet search

Lennart Luettgau, Hannah Rose Kirk, Kobi Hackenburg +6

Conversational AI systems are increasingly being used in place of traditional search engines to help users complete information-seeking tasks. This has raised concerns in the polit…

cs.HC20261 cited

Ask don't tell: Reducing sycophancy in large language models

Magda Dubois, Cozmin Ududec, Christopher Summerfield +1

Sycophancy, the tendency of large language models to favour user-affirming responses over critical engagement, has been identified as an alignment failure, particularly in high-sta…

cs.HC2026

People readily follow personal advice from AI but it does not improve their well-being

Lennart Luettgau, Vanessa Cheung, Magda Dubois +8

People increasingly seek personal advice from large language models (LLMs), yet whether humans follow their advice, and its consequences for their well-being, remains unknown. In a…

cs.HC20262 cited

Neural steering vectors reveal dose and exposure-dependent impacts of human-AI relationships

Hannah Rose Kirk, Henry Davidson, Ed Saunders +4

Humans are increasingly forming parasocial relationships with AI systems, and modern AI shows an increasing tendency to display social and relationship-seeking behaviour. However,…

cs.HC2026

Disclosure By Design: Identity Transparency as a Behavioural Property of Conversational AI Models

Anna Gausen, Sarenne Wallbridge, Hannah Rose Kirk +2

As conversational AI systems become more realistic and widely deployed, users are increasingly uncertain about whether they are interacting with a human or an AI system. When AI id…