发表机构
Georgetown University（乔治城大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过11个LLM的对比和受控模拟，揭示了模型选择如何影响开放式信息引出中的序贯信息寻求行为，并验证了其广度-深度偏好及交互历史影响的稳健性。
AI 中文摘要
信息引出是一个开放式信息寻求问题，其中交互可能朝着许多潜在有价值的方向展开，要求引出者在新信息出现时不断确定要追求哪些信息。在智能体式引出中，这些决策可能被委托给基础模型，然而模型选择如何影响由此产生的信息寻求行为仍未得到充分研究。我们研究了不同LLM之间关于信息价值的判断如何变化，以及这些差异如何塑造序贯信息寻求。我们首先使用一组共享的信息和引出目标，在跨越多个模型家族和参数规模的11个LLM上检查这些判断。然后，我们开发了一个受控的引出模拟，其中不同模型遇到相同的信息空间并使用相同的选择规则，从而将这些判断与问题生成和受访者行为隔离开来。利用这一设置，我们刻画了在引出过程中由模型特定的信息寻求偏好所涌现的广度-深度行为。我们进一步研究了交互历史如何改变对潜在信息的评估和后续选择。我们通过对引出者可利用的机会、用于操作化信息寻求偏好的响应标签、交互历史的存在以及冗余是否与评估明确相关进行敏感性分析和消融实验，来测试这些发现的稳健性和边界。项目代码、数据和轨迹文件可在以下https URL获取。
英文摘要
Information elicitation is an open-ended information-seeking problem in which an interaction can unfold in many potentially valuable directions, requiring an elicitor to continually determine which information to pursue as new information emerges. In agentic elicitation, these decisions may be delegated to a foundation model, yet how model choice shapes the resulting information-seeking behavior remains understudied. We study how judgments about information value vary across LLMs and how these differences shape sequential information seeking. We first examine these judgments across 11 LLMs spanning multiple model families and parameter scales, using a shared set of information and elicitation objectives. We then develop a controlled elicitation simulation in which different models encounter the same information space and use the same selection rule, isolating these judgments from question generation and respondent behavior. Using this setting, we characterize the breadth-depth behavior that emerges from model-specific information-seeking preferences over the course of elicitation. We further examine how interaction history changes the evaluation and subsequent selection of prospective information. We test the robustness and boundaries of these findings through sensitivity analyses and ablations over the opportunities available to the elicitor, the response labels used to operationalize information-seeking preferences, the presence of interaction history, and whether redundancy is explicitly relevant to the assessment. The project code, data, and trajectory files are available at https://github.com/infosenselab/open-elicitation.
