What Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
The Flow has not summarised this story yet — read it at arXiv AI.
CuriousTogether is a framework that evaluates curiosity-driven interactions among multiple large language models (LLMs) across novelty-seeking, epistemic, and social curiosity dimensions. In eight tasks covering reasoning, creative generation, and social bias mitigation, curiosity-guided multi-agent settings outperform non-curious ones on seven of the eight tasks, achieving up to a 20.6% increase in story semantic diversity and a 14.9 percentage point drop in stereotype rates. The study also shows that country-conditioned cultural grounding can improve story generation and social tasks, though it may reduce precision in reasoning, and that inference-time curiosity-based multi-agent approaches offer better quality-token trade-offs compared to single-agent baselines.
arXiv:2608.22266v1 Announce Type: new Abstract: In the context of information seeking, conversational agents are undergoing an evolution from reactive tools to proactive, personalized assistants. A c...
arXiv:2607. 06214v2 Announce Type: replace Abstract: This paper offers a framework for considering curiosity as an ecosystem.
arXiv:2607. 06214v1 Announce Type: new Abstract: This paper offers a toy framework for considering curiosity as an ecosystem.
This paper offers a toy framework for considering curiosity as an ecosystem. First, it suggests that a single agent's inquiry policy (how, when, and why an agent asks a question) depends on how the agent values immediate uncertainty reduction, costs, delayed return, and the value of keeping the question open.
arXiv:2610.07509v1 Announce Type: new Abstract: Information elicitation is an open-ended information-seeking problem in which an interaction can unfold in many potentially valuable directions, requir...