{"id":"W4399655979","doi":"10.48550/arxiv.2406.07467","title":"LLM meets ML: Data-efficient Anomaly Detection on Unstable Logs","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Alliance de recherche numérique du Canada; Fonds National de la Recherche Luxembourg; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Anomaly detection; Anomaly (physics); Computer science; Data mining; Physics","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":[0.002667204,0.00167059,0.001270177,0.002457796,0.0006516603,0.001502572,0.002925729,0.001380835,0.001503358],"category_scores_gemma":[0.01026109,0.0004629349,0.0008788276,0.001550437,0.0005299664,0.003662605,0.001951469,0.001975753,0.002019985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009095906,"about_ca_system_score_gemma":0.001598064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008824712,"about_ca_topic_score_gemma":0.01305418,"domain_scores_codex":[0.9980596,0.0004141005,0.0001479615,0.0005543963,0.0006295597,0.0001943588],"domain_scores_gemma":[0.9957463,0.001543587,0.0002671457,0.001402173,0.0008568186,0.0001839674],"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":[0.0007651714,0.0007291074,0.03516696,0.0002807857,0.0002166045,0.0003255305,0.0001686269,0.09649543,0.01316641,0.002168611,0.0477742,0.8027426],"study_design_scores_gemma":[0.00003289375,0.0001263799,0.003346201,0.00001388999,0.00001886456,0.0001345054,0.00007474908,0.9795999,0.008114235,0.004316683,0.004195096,0.00002661045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2173224,0.002205752,0.631775,0.001414168,0.0004912651,0.0002752913,0.007424397,0.1347144,0.004377283],"genre_scores_gemma":[0.7012662,0.0004027117,0.2750698,0.000392702,0.0001655848,0.0002005046,0.01763819,0.0009667255,0.003897612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008824712,"threshold_uncertainty_score":0.01754665,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09849332833791093,"score_gpt":0.2092229584217083,"score_spread":0.1107296300837973,"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."}}