Output Elicitation

Follow this topic Ask about this topic

Latest papers

8 papers1 paper in the last four weeks, with none the four weeks before. 0.0% of all new papers.

On Open-Ended Information Seeking for Information Elicitation Agents
Oct 5, 2026Victor De Lima, Grace Hui YangValue of Information Output Elicitation
InfoSense Lab, Georgetown University
Bayes-Sufficient Representations in Supervised Learning
Jun 2, 2026Vasileios SevetlidisRepresentation Learning Output Elicitation
Athena Research Center, Kimmeria Campus, Xanthi, Greece · Democritus University of Thrace, Vas. Sofias Campus, Xanthi, Greece · International Hellenic University, Serres, Greece
BC Protocol: Structured Dual-Expert Dialogue for Eliciting High-Quality Chain-of-Thought Post-Training Data
May 25, 2026Bo Zou, Chao XuOutput Elicitation Synthetic Data Generation for Language Models
YIELD: A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents
Apr 13, 2026Victor De Lima, Grace Hui YangOutput Elicitation LLM Agent Evaluation
InfoSense Lab, Georgetown University
Expert Mind: A Retrieval-Augmented Architecture for Expert Knowledge Preservation in the Energy Sector
Mar 15, 2026Diego Ezequiel CerveraOutput Elicitation Knowledge Representation
Independent Researcher, Buenos Aires, Argentina
Elenchus: Generating Knowledge Bases from Prover-Skeptic Dialogues
Mar 7, 2026Bradley P. AllenOutput Elicitation Knowledge Representation
University of Amsterdam
Beyond the Resumé: A Rubric-Aware Automatic Interview System for Information Elicitation
Mar 2, 2026Harry Stuart, Masahiro Kaneko, Timothy BaldwinOutput Elicitation AI and Labor
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, United Arab Emirates
Power and Limitations of Aggregation in Compound AI Systems
Feb 25, 2026Nivasini Ananthakrishnan, Meena JagadeesanMulti-Agent LLM Systems Principal-Agent Theory
UC Berkeley · Stanford University

Oct 5, 2026J/K move · Enter open · S save

On Open-Ended Information Seeking for Information Elicitation Agents

Victor De Lima, Grace Hui Yang

InfoSense Lab, Georgetown University

Value of Information Output Elicitation LLM Decision-Making

Open paper AskPDF arXiv

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.