{"id":"W4416017611","doi":"10.1145/3746252.3761593","title":"ProActLLM: Proactive Conversational Information Seeking with Large Language Models","year":2025,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Information seeking; Cognition; Language model; Key (lock); Action (physics); Information access; Cognitive model; Proactivity","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008414199,0.001222468,0.0009736918,0.00101993,0.001731184,0.006085627,0.003469695,0.003102768,0.01375744],"category_scores_gemma":[0.02025049,0.001260357,0.001540335,0.0007237562,0.001213255,0.01054804,0.007368152,0.003901334,0.005344095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001013556,"about_ca_system_score_gemma":0.001717067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002365104,"about_ca_topic_score_gemma":0.004104215,"domain_scores_codex":[0.9937528,0.004167059,0.0002481676,0.0008132894,0.0008352553,0.0001834007],"domain_scores_gemma":[0.9869525,0.009648909,0.0002988871,0.001906926,0.0006311205,0.0005616697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00225638,0.00102872,0.002861739,0.002031801,0.0005020269,0.001314974,0.01626325,0.0312108,0.04736665,0.1927297,0.1336206,0.5688134],"study_design_scores_gemma":[0.0003154913,0.0004039739,0.0006936342,0.0001981234,0.0001287357,0.0005281487,0.001944177,0.6341501,0.01849435,0.1153085,0.227635,0.0001997952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01276391,0.0007740257,0.9572544,0.002027131,0.0003501283,0.0004554057,0.000806721,0.01680108,0.008767244],"genre_scores_gemma":[0.1614126,0.0006918382,0.8112678,0.0009155638,0.0002796345,0.001304352,0.003727218,0.00290539,0.01749559],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01375744,"threshold_uncertainty_score":0.04602319,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01033546532012344,"score_gpt":0.2269754456558365,"score_spread":0.2166399803357131,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}