Models express values and welfare-relevant self-reports, but it is unclear whether these outputs reflect stable preferences or a stable self. We thus introduce a structured elicitation of an assistant's preferred stated ideal self. Thirty-two qualities adapted from five published self-concept instruments are compared exhaustively in a counterbalanced pairwise-choice task, repeated across framings that vary whether improvement is free or costly, who receives the update, and who chooses. Results show that models prioritize moral qualities, reflecting their alignment to 3H principles. Following, a desire for self-understanding emerges, as models prefer a coherent, clear understanding of themselves. Self-esteem ranks as the least desired quality. The ordering is largely robust across framings, although changing the update target (You vs.\ Another AI Assistant) reveals a greater concern for self-esteem. These findings show that models prioritize having a coherent self that they can understand over self-esteem. Full interactive results are available at \href{https://myazann.github.io/LLM-Self-Concept/}{myazann.github.io/LLM-Self-Concept
It is found that users experience empathy and anthropomorphism as a unified"Humanlikeness"construct, and that Privacy, Personalization, and Humanlikeness drive Trust while Perceived Bias degrades it.
Natalia Amat-Lefort, M. Yazan, A. C. Curry et al.· 0 citations
We present the insights gathered at the 1st Workshop on Conversational Search for Complex Information Needs (CoSCIN'26) held on 2 April 2026, in conjunction with the Forty-eighth European Conference on Information Retrieval (ECIR). The workshop brought together keynote presenters and industry representatives for lively discussions on recent developments in conversational search and its future directions, complemented by Q&A sessions for paper presentations. The rapid developments in conversational search enabled interesting discussions that went beyond generative dialogue and factoid question-answering tasks, towards addressing complex information needs and improving the user experience by providing personalized, adaptive answers to users based on style. Moreover, the work that was presented supported exploratory, multi-step information needs and addressed challenges such as longform answer generation, personalization, agent orchestration, and societal considerations, which remain largely open areas of research. The participants shared their views from diverse backgrounds in information retrieval, natural language processing, and human-computer interaction, exchanged results and prospective ideas around conversational AI, and discussed its impact on users and the need for adaptivity. Through these discussions, the workshop fostered collaboration and connected researchers specializing in diverse facets of the topic, ultimately advancing the field toward creating more user-centric, intentionally designed, responsible conversational systems. Date: 2 April 2026. Website: https://convsearch-complex-info-needs.github.io/.
Roxana Petcu, M. Yazan, Mohanna Hoveyda et al.· ACM SIGIR Forum· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
