{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4SDVHVH3FJCFZLL5SJWAW3FSFH","short_pith_number":"pith:4SDVHVH3","schema_version":"1.0","canonical_sha256":"e48753d4fb2a445cad7d926c0b6cb229caf1ac0ef214a6443682ced214ede3df","source":{"kind":"arxiv","id":"2506.07424","version":1},"attestation_state":"computed","paper":{"title":"Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jinhee Jang, Juhwan Choi, Kyeonghyun Kim, Kyohoon Jin, Yoonji Lee, YoungBin Kim","submitted_at":"2025-06-09T04:45:13Z","abstract_excerpt":"Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable for resource-constrained environments. In contrast, small language models (SLMs) are computationally efficient but often lack the broad generalization capacity of LLMs. To bridge this gap, we propose PiFi, a novel framework that combines the strengths of both LLMs and SLMs to achieve high performance while maintaining efficiency. PiFi integrates a single frozen layer from an LLM into a SLM and fine-tunes the combine"},"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":"2506.07424","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-09T04:45:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"3b458d80b06c8464a3b8e87a72115b47db81411b23defc6ae165d3d143ce6cc7","abstract_canon_sha256":"b51409d7ef50fec914866c51991680655bdda604bc0e7895c66866eb1a64108e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:18:27.052727Z","signature_b64":"p0rilPpAPcvbnd8DMmZG/bQnC1CRZtPOZ/ugesrNkjzC/8rOIzqiAGKCtzVr3KDFmtpND+Mr0CGK353CbjwMAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e48753d4fb2a445cad7d926c0b6cb229caf1ac0ef214a6443682ced214ede3df","last_reissued_at":"2026-07-05T11:18:27.052259Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:18:27.052259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Plug-in and Fine-tuning: Bridging the Gap between Small Language Models and Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Jinhee Jang, Juhwan Choi, Kyeonghyun Kim, Kyohoon Jin, Yoonji Lee, YoungBin Kim","submitted_at":"2025-06-09T04:45:13Z","abstract_excerpt":"Large language models (LLMs) are renowned for their extensive linguistic knowledge and strong generalization capabilities, but their high computational demands make them unsuitable for resource-constrained environments. In contrast, small language models (SLMs) are computationally efficient but often lack the broad generalization capacity of LLMs. To bridge this gap, we propose PiFi, a novel framework that combines the strengths of both LLMs and SLMs to achieve high performance while maintaining efficiency. PiFi integrates a single frozen layer from an LLM into a SLM and fine-tunes the combine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.07424","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/2506.07424/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":"2506.07424","created_at":"2026-07-05T11:18:27.052321+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.07424v1","created_at":"2026-07-05T11:18:27.052321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.07424","created_at":"2026-07-05T11:18:27.052321+00:00"},{"alias_kind":"pith_short_12","alias_value":"4SDVHVH3FJCF","created_at":"2026-07-05T11:18:27.052321+00:00"},{"alias_kind":"pith_short_16","alias_value":"4SDVHVH3FJCFZLL5","created_at":"2026-07-05T11:18:27.052321+00:00"},{"alias_kind":"pith_short_8","alias_value":"4SDVHVH3","created_at":"2026-07-05T11:18:27.052321+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/4SDVHVH3FJCFZLL5SJWAW3FSFH","json":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH.json","graph_json":"https://pith.science/api/pith-number/4SDVHVH3FJCFZLL5SJWAW3FSFH/graph.json","events_json":"https://pith.science/api/pith-number/4SDVHVH3FJCFZLL5SJWAW3FSFH/events.json","paper":"https://pith.science/paper/4SDVHVH3"},"agent_actions":{"view_html":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH","download_json":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH.json","view_paper":"https://pith.science/paper/4SDVHVH3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.07424&json=true","fetch_graph":"https://pith.science/api/pith-number/4SDVHVH3FJCFZLL5SJWAW3FSFH/graph.json","fetch_events":"https://pith.science/api/pith-number/4SDVHVH3FJCFZLL5SJWAW3FSFH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH/action/storage_attestation","attest_author":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH/action/author_attestation","sign_citation":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH/action/citation_signature","submit_replication":"https://pith.science/pith/4SDVHVH3FJCFZLL5SJWAW3FSFH/action/replication_record"}},"created_at":"2026-07-05T11:18:27.052321+00:00","updated_at":"2026-07-05T11:18:27.052321+00:00"}