{"id":"W4403896196","doi":"10.1111/cgf.15266","title":"Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions","year":2024,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Centre International de Recherche sur le Cancer","keywords":"Computer science; State (computer science); Visualization; Natural (archaeology); Computer graphics (images); Natural language generation; Human–computer interaction; Natural language; Artificial intelligence; Programming language; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01410667,0.001277883,0.001350795,0.004551004,0.001029623,0.01018685,0.004639375,0.004621531,0.01509874],"category_scores_gemma":[0.03634973,0.0008935806,0.001569734,0.006150415,0.003210633,0.01765378,0.002854716,0.004874862,0.005421487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002060956,"about_ca_system_score_gemma":0.00339566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003461104,"about_ca_topic_score_gemma":0.001976206,"domain_scores_codex":[0.9909696,0.00446591,0.0006240782,0.001240021,0.002224394,0.0004760088],"domain_scores_gemma":[0.9184453,0.06451434,0.001529454,0.00418565,0.01026071,0.00106453],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001214823,0.0002391403,0.001470184,0.01065408,0.0000634461,0.000193958,0.002865302,0.003406001,0.002392905,0.09322643,0.053126,0.8322411],"study_design_scores_gemma":[0.00006253959,0.0001648791,0.001818326,0.01078729,0.00008856886,0.0007068302,0.005784833,0.0245373,0.003783503,0.1445378,0.807497,0.0002310963],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.009725837,0.7828783,0.132211,0.04791025,0.001905706,0.0002331721,0.0008055928,0.004151844,0.02017827],"genre_scores_gemma":[0.09601806,0.6606119,0.2182959,0.007864087,0.003709029,0.0006368289,0.003144981,0.00140506,0.008314067],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01509874,"threshold_uncertainty_score":0.07460415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01966146682931232,"score_gpt":0.2910691527554301,"score_spread":0.2714076859261178,"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."}}