{"id":"W4412889553","doi":"10.18653/v1/2025.acl-short.48","title":"Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis","year":2025,"lang":"en","type":"article","venue":"","topic":"Sentiment Analysis and Opinion Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Chung-Ang University","keywords":"Computer science; Generative grammar; Artificial intelligence; Order (exchange); Sentiment analysis; Generative model; Natural language processing; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002502967,0.0001193007,0.0002101144,0.0005717053,0.0001960428,0.0001867494,0.0002814604,0.00004084449,0.0001242802],"category_scores_gemma":[0.00001230898,0.0001015736,0.0002409326,0.00217627,0.00001454336,0.0001644273,0.00006594554,0.00003999727,0.00001268658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007036942,"about_ca_system_score_gemma":0.00006810923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002302557,"about_ca_topic_score_gemma":0.00005915409,"domain_scores_codex":[0.9988856,0.00003967939,0.0002755867,0.0004346389,0.0001814152,0.000183078],"domain_scores_gemma":[0.9992677,0.00007881568,0.00007942366,0.0003730077,0.0001627973,0.00003823264],"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":[0.00009536895,0.001112534,0.07612273,0.00009113757,0.01941139,0.000009156784,0.001044935,0.5196252,0.01193915,0.265291,0.05718572,0.04807167],"study_design_scores_gemma":[0.0004044914,0.00003137561,0.003212054,0.000006066046,0.0003402619,7.256489e-8,0.00003187668,0.9888237,0.004829498,0.0004314573,0.001792863,0.00009628513],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004026629,0.00006160881,0.9918666,0.001679579,0.0002554779,0.0001882181,0.000005953905,0.0001176674,0.001798269],"genre_scores_gemma":[0.563054,0.000006633348,0.4219651,0.001142446,0.00002928485,0.000100665,0.0001294844,0.000007066208,0.01356534],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5699015,"threshold_uncertainty_score":0.414205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01237440331190987,"score_gpt":0.2874402406226955,"score_spread":0.2750658373107857,"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."}}