{"id":"W4385734218","doi":"10.18653/v1/2023.matching-1.7","title":"Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering","year":2023,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Question answering; Computer science; Shot (pellet); Knowledge graph; Graph; Task (project management); Zero (linguistics); Domain knowledge; Natural language processing; Artificial intelligence; Information retrieval; Theoretical computer science; 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":[0.002018924,0.001810062,0.001075908,0.0008070093,0.0005530274,0.001289428,0.002500413,0.002404248,0.008675625],"category_scores_gemma":[0.01203023,0.0005265363,0.000938855,0.0005398327,0.0009470495,0.005014481,0.003551623,0.003342068,0.003870538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009176973,"about_ca_system_score_gemma":0.001375325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003659853,"about_ca_topic_score_gemma":0.006632769,"domain_scores_codex":[0.9979603,0.0009026164,0.00008417767,0.0006427499,0.000280385,0.0001297107],"domain_scores_gemma":[0.9950204,0.00332325,0.0001640464,0.000845843,0.0004350783,0.0002114339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001753668,0.000991486,0.002686086,0.001810993,0.0001701479,0.0008366502,0.002588804,0.07591418,0.06530229,0.01676043,0.03966818,0.7915171],"study_design_scores_gemma":[0.0001651562,0.0004858955,0.0008764446,0.0000697428,0.00009284185,0.000336211,0.000540024,0.8924906,0.02799722,0.0563655,0.02049326,0.00008721094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03561062,0.0009284869,0.9017234,0.0006953449,0.000230851,0.0003351503,0.001275998,0.05621092,0.0029893],"genre_scores_gemma":[0.5705777,0.0003714627,0.4166493,0.001094533,0.0001559626,0.0004459415,0.00458746,0.001043189,0.005074437],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008675625,"threshold_uncertainty_score":0.02902287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06291405592591279,"score_gpt":0.3394715682400002,"score_spread":0.2765575123140874,"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."}}