{"id":"W4411143753","doi":"10.1109/icaace65325.2025.11019781","title":"Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA, and ChatGPT","year":2025,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"End-to-end principle; Zero (linguistics); Shot (pellet); Relation (database); Extraction (chemistry); Computer science; Mathematics; Artificial intelligence; Chromatography; Materials science; Chemistry; Data mining; Linguistics","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":[0.005050067,0.00191376,0.001509855,0.002613014,0.001322076,0.001906094,0.002542214,0.00103727,0.002673614],"category_scores_gemma":[0.01465051,0.000541238,0.001192926,0.002319972,0.0009484428,0.00654743,0.00240187,0.0015224,0.00202511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001512602,"about_ca_system_score_gemma":0.00282683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03660667,"about_ca_topic_score_gemma":0.05074232,"domain_scores_codex":[0.9973248,0.0007887836,0.0002680739,0.0009720607,0.0004391385,0.0002070338],"domain_scores_gemma":[0.9935933,0.004002789,0.0001784714,0.001258656,0.0007538409,0.0002130339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002895874,0.0007269258,0.02945893,0.002651611,0.0009311574,0.00165835,0.003255857,0.03490269,0.02780646,0.004672518,0.03051441,0.8605252],"study_design_scores_gemma":[0.0002937805,0.001240666,0.04544981,0.0002395751,0.001072645,0.002499779,0.004763126,0.8148513,0.07587951,0.009887308,0.04344448,0.0003779054],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.760631,0.009709532,0.1647556,0.001564733,0.0006366456,0.0007934365,0.00996897,0.03816039,0.01377958],"genre_scores_gemma":[0.8234739,0.001976887,0.1379719,0.0005082701,0.0001218753,0.0002938679,0.02809922,0.001103387,0.00645071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03660667,"threshold_uncertainty_score":0.07278717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04419024323071791,"score_gpt":0.3451886114867058,"score_spread":0.3009983682559879,"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."}}