{"id":"W4409167231","doi":"10.1007/978-3-031-88708-6_3","title":"Is Relevance Propagated from Retriever to Generator in RAG?","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Topic Modeling","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Relevance (law); Generator (circuit theory); Labrador Retriever; Medicine; Physics; Political science; Law; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006756089,0.0005491735,0.0006330279,0.00104055,0.0001375055,0.0004783121,0.004283382,0.0004037747,0.00003018508],"category_scores_gemma":[0.0002806623,0.0005313943,0.0000986621,0.001507562,0.0001917417,0.0005044042,0.002124644,0.0009879171,0.0000680361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005730427,"about_ca_system_score_gemma":0.0009981599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001740106,"about_ca_topic_score_gemma":0.0002300807,"domain_scores_codex":[0.9949593,0.00004479084,0.00072051,0.002522854,0.001018325,0.0007342374],"domain_scores_gemma":[0.9967476,0.0004242394,0.0001884902,0.00217819,0.0002616611,0.0001998511],"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":[0.000017845,0.00003545112,0.0003129358,0.00004706538,0.00001497398,0.000245474,0.002111185,0.0506027,0.0009874878,0.009943251,0.0004434749,0.9352381],"study_design_scores_gemma":[0.0003728955,0.00007971074,0.0001960034,0.00104186,0.000005021167,0.000007828233,1.129798e-7,0.8892704,0.008490018,0.09224282,0.007434388,0.0008589335],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000881955,0.0005697423,0.9910708,0.003042912,0.002263006,0.00060453,0.00001368771,0.0001595877,0.00139376],"genre_scores_gemma":[0.06799356,0.00005163825,0.9160896,0.01324775,0.0005616369,0.00002176033,0.000005433355,0.00003710037,0.001991521],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9343792,"threshold_uncertainty_score":0.9997138,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01878913682042273,"score_gpt":0.2455350456091588,"score_spread":0.2267459087887361,"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."}}