{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CAPO726K4A6GOMZPC6RU5FFCCN","short_pith_number":"pith:CAPO726K","schema_version":"1.0","canonical_sha256":"101eefebcae03c67332f17a34e94a21373959ec8aec0fd205f70ee8c106efeb5","source":{"kind":"arxiv","id":"2310.15594","version":1},"attestation_state":"computed","paper":{"title":"Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dongyan Zhao, Jiahao Liu, Jiduan Liu, Jingang Wang, Qifan Wang, Ran Lucien Wang, Rui Yan, Xunliang Cai","submitted_at":"2023-10-24T07:58:20Z","abstract_excerpt":"Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. While numerous model compression techniques have been proposed, most of them are not well-suited for achieving extreme model compression when there is a significant gap in model scale. In this paper, we introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT), which effectively transfers the knowledge of LLMs t"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.15594","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-24T07:58:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f7e0a29962e14fcf3972ad34fd637a94da4a0657c938d8612b2a31a3bec46567","abstract_canon_sha256":"5132debcd7b2a37389c42e705c9bb4b3d1bc4d3dd3f6394a5f2233adde006670"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:04:28.370396Z","signature_b64":"WkNe4IzT+CTgmj/tGX2MuWdOjlPrjwLTG+7JS6LvTNrOTwM+FqK15x/0z1dqguJyP5RN5FYN+jsjCZISavirAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"101eefebcae03c67332f17a34e94a21373959ec8aec0fd205f70ee8c106efeb5","last_reissued_at":"2026-07-05T07:04:28.370008Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:04:28.370008Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dongyan Zhao, Jiahao Liu, Jiduan Liu, Jingang Wang, Qifan Wang, Ran Lucien Wang, Rui Yan, Xunliang Cai","submitted_at":"2023-10-24T07:58:20Z","abstract_excerpt":"Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. However, the massive size of these models poses huge challenges for their deployment in real-world applications. While numerous model compression techniques have been proposed, most of them are not well-suited for achieving extreme model compression when there is a significant gap in model scale. In this paper, we introduce a novel compression paradigm called Retrieval-based Knowledge Transfer (RetriKT), which effectively transfers the knowledge of LLMs t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.15594","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.15594/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.15594","created_at":"2026-07-05T07:04:28.370062+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.15594v1","created_at":"2026-07-05T07:04:28.370062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.15594","created_at":"2026-07-05T07:04:28.370062+00:00"},{"alias_kind":"pith_short_12","alias_value":"CAPO726K4A6G","created_at":"2026-07-05T07:04:28.370062+00:00"},{"alias_kind":"pith_short_16","alias_value":"CAPO726K4A6GOMZP","created_at":"2026-07-05T07:04:28.370062+00:00"},{"alias_kind":"pith_short_8","alias_value":"CAPO726K","created_at":"2026-07-05T07:04:28.370062+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN","json":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN.json","graph_json":"https://pith.science/api/pith-number/CAPO726K4A6GOMZPC6RU5FFCCN/graph.json","events_json":"https://pith.science/api/pith-number/CAPO726K4A6GOMZPC6RU5FFCCN/events.json","paper":"https://pith.science/paper/CAPO726K"},"agent_actions":{"view_html":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN","download_json":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN.json","view_paper":"https://pith.science/paper/CAPO726K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.15594&json=true","fetch_graph":"https://pith.science/api/pith-number/CAPO726K4A6GOMZPC6RU5FFCCN/graph.json","fetch_events":"https://pith.science/api/pith-number/CAPO726K4A6GOMZPC6RU5FFCCN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN/action/storage_attestation","attest_author":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN/action/author_attestation","sign_citation":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN/action/citation_signature","submit_replication":"https://pith.science/pith/CAPO726K4A6GOMZPC6RU5FFCCN/action/replication_record"}},"created_at":"2026-07-05T07:04:28.370062+00:00","updated_at":"2026-07-05T07:04:28.370062+00:00"}