{"id":"W4385767747","doi":"10.24963/ijcai.2023/460","title":"On Conditional and Compositional Language Model Differentiable Prompting","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Modular design; Principle of compositionality; Generalization; Automatic summarization; Task (project management); Artificial intelligence; Word (group theory); Natural language processing; Embedding; Language model; Artificial neural network; Human–computer interaction; Programming language","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":[0.001476727,0.0008058283,0.0006413082,0.0002970069,0.0002828562,0.0006656728,0.001348485,0.0008774068,0.003739284],"category_scores_gemma":[0.006493441,0.0004458593,0.0006541793,0.0004275603,0.001042019,0.002994399,0.001538236,0.002217047,0.001094703],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006679253,"about_ca_system_score_gemma":0.0007603387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001995875,"about_ca_topic_score_gemma":0.002611022,"domain_scores_codex":[0.9994271,0.0002644395,0.0000240035,0.0001784802,0.00005876975,0.00004730522],"domain_scores_gemma":[0.9979852,0.001250537,0.0001131764,0.0003910149,0.0001883269,0.0000717034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002891513,0.0001441379,0.001064709,0.0002425809,0.00005959329,0.0002169378,0.0004814375,0.724988,0.01382222,0.04077484,0.003455992,0.2144604],"study_design_scores_gemma":[0.00001225324,0.00004983998,0.0001312324,0.000006068852,0.000006588475,0.00002464403,0.00001753592,0.9741823,0.001890648,0.02298727,0.0006829452,0.000008688547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05564792,0.0003462668,0.9373673,0.0003481116,0.00007374837,0.00005939133,0.0001898783,0.00348574,0.002481655],"genre_scores_gemma":[0.8204641,0.0003403467,0.1698323,0.0002681624,0.0000778954,0.000212449,0.0006924861,0.0004631947,0.007649232],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003739284,"threshold_uncertainty_score":0.01250917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02142413402004007,"score_gpt":0.2602325658297621,"score_spread":0.2388084318097221,"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."}}