Eleven LLMs differ in which information they judge worth pursuing, producing markedly different breadth and depth in open-ended elicitation trajectories
Using 142 arguments from IBM-Rank-30k as a shared information space, the study feeds 11 open-weight instruction-tuned LLMs' judgments of whether a candidate piece of information is worth pursuing (the probability of an affirmative label, treated as an information-seeking score) into one sequential elicitation simulation with a fixed selection rule, fixed respondent behavior, and no natural-language question generation, characterizing how model-specific information-seeking preferences shape the breadth and depth of elicitation trajectories and testing the effects of interaction history, candidate-set size, response labels, and prompt wording.