{"id":"W4403813921","doi":"10.48550/arxiv.2409.19747","title":"Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Centre International de Recherche sur le Cancer","keywords":"Natural (archaeology); State (computer science); Computer science; Visualization; Human–computer interaction; Data science; Geology; Programming language; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01207577,0.001284758,0.001352643,0.004841569,0.001220683,0.01047845,0.003868009,0.003884906,0.01770666],"category_scores_gemma":[0.03267882,0.0009079541,0.001571586,0.006956525,0.003231161,0.02229393,0.002914571,0.005129967,0.007408909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002423828,"about_ca_system_score_gemma":0.004253896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004923749,"about_ca_topic_score_gemma":0.003321498,"domain_scores_codex":[0.9930363,0.003034934,0.000505313,0.001104629,0.001906935,0.0004117253],"domain_scores_gemma":[0.9367206,0.04896952,0.00132253,0.003426951,0.008596079,0.000964325],"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":[0.00009835121,0.0001936051,0.001292795,0.007806878,0.00004779611,0.0001471342,0.002037532,0.001962804,0.001958609,0.07233413,0.0487763,0.8633441],"study_design_scores_gemma":[0.00005048547,0.0001651172,0.002024559,0.009460795,0.00008783326,0.0007062816,0.00521067,0.01709965,0.003024367,0.1611962,0.8007644,0.0002096299],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00743545,0.8006245,0.1133172,0.04966024,0.002006159,0.000210467,0.0007579201,0.003803414,0.02218454],"genre_scores_gemma":[0.04720167,0.7626102,0.1662386,0.007562245,0.003878453,0.0004576922,0.002519483,0.001144038,0.008387592],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01770666,"threshold_uncertainty_score":0.06386352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06189839220070732,"score_gpt":0.2383711742443884,"score_spread":0.1764727820436811,"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."}}