{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KHDA2RFFAIT6TZF7HQJP4CINEI","short_pith_number":"pith:KHDA2RFF","schema_version":"1.0","canonical_sha256":"51c60d44a50227e9e4bf3c12fe090d220f038dc13a258a8ed9a26297595f858d","source":{"kind":"arxiv","id":"2312.15692","version":4},"attestation_state":"computed","paper":{"title":"Instruction Fusion: Advancing Prompt Evolution through Hybridization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Di Niu, Jiuding Yang, Kaitong Yang, Weidong Guo, Xiangyang Li, Yu Xu, Zhuwei Rao","submitted_at":"2023-12-25T11:00:37Z","abstract_excerpt":"The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries. Despite the successes, existing methodologies like Evol-Instruct encounter performance limitations, impeding further enhancements in code generation tasks. This paper examines the constraints of existing prompt evolution techniques and introduces a novel approach, Instruction Fusion (IF). IF innovatively combines two distinct prompts through a hybridization process, thereby enhancing the evolution of training prompts for code LLMs. Our expe"},"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":"2312.15692","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2023-12-25T11:00:37Z","cross_cats_sorted":[],"title_canon_sha256":"eb21b132fba1117d38687726d3b24397f130f676f5246e0956c126cb353bf06f","abstract_canon_sha256":"0cc164f31b7eb473f0e87b70d1664f73c5d38c70c8ccf0982410431c737de86e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:32:34.201788Z","signature_b64":"xRzRAn/+4AQQNJ1pBlfQf7jZhWcVHQx70Siv/TVgYn/cOwLmz3lDYOZVQ7qB49Ge6YYwNMKEdNnXVW5UYYWRCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51c60d44a50227e9e4bf3c12fe090d220f038dc13a258a8ed9a26297595f858d","last_reissued_at":"2026-07-05T08:32:34.201267Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:32:34.201267Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Instruction Fusion: Advancing Prompt Evolution through Hybridization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Di Niu, Jiuding Yang, Kaitong Yang, Weidong Guo, Xiangyang Li, Yu Xu, Zhuwei Rao","submitted_at":"2023-12-25T11:00:37Z","abstract_excerpt":"The fine-tuning of Large Language Models (LLMs) specialized in code generation has seen notable advancements through the use of open-domain coding queries. Despite the successes, existing methodologies like Evol-Instruct encounter performance limitations, impeding further enhancements in code generation tasks. This paper examines the constraints of existing prompt evolution techniques and introduces a novel approach, Instruction Fusion (IF). IF innovatively combines two distinct prompts through a hybridization process, thereby enhancing the evolution of training prompts for code LLMs. Our expe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15692","kind":"arxiv","version":4},"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/2312.15692/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":"2312.15692","created_at":"2026-07-05T08:32:34.201328+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.15692v4","created_at":"2026-07-05T08:32:34.201328+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15692","created_at":"2026-07-05T08:32:34.201328+00:00"},{"alias_kind":"pith_short_12","alias_value":"KHDA2RFFAIT6","created_at":"2026-07-05T08:32:34.201328+00:00"},{"alias_kind":"pith_short_16","alias_value":"KHDA2RFFAIT6TZF7","created_at":"2026-07-05T08:32:34.201328+00:00"},{"alias_kind":"pith_short_8","alias_value":"KHDA2RFF","created_at":"2026-07-05T08:32:34.201328+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2402.13116","citing_title":"A Survey on Knowledge Distillation of Large Language Models","ref_index":208,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI","json":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI.json","graph_json":"https://pith.science/api/pith-number/KHDA2RFFAIT6TZF7HQJP4CINEI/graph.json","events_json":"https://pith.science/api/pith-number/KHDA2RFFAIT6TZF7HQJP4CINEI/events.json","paper":"https://pith.science/paper/KHDA2RFF"},"agent_actions":{"view_html":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI","download_json":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI.json","view_paper":"https://pith.science/paper/KHDA2RFF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.15692&json=true","fetch_graph":"https://pith.science/api/pith-number/KHDA2RFFAIT6TZF7HQJP4CINEI/graph.json","fetch_events":"https://pith.science/api/pith-number/KHDA2RFFAIT6TZF7HQJP4CINEI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI/action/storage_attestation","attest_author":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI/action/author_attestation","sign_citation":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI/action/citation_signature","submit_replication":"https://pith.science/pith/KHDA2RFFAIT6TZF7HQJP4CINEI/action/replication_record"}},"created_at":"2026-07-05T08:32:34.201328+00:00","updated_at":"2026-07-05T08:32:34.201328+00:00"}