{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:N6LEERFVX3L7IWR4JOV62JN2VE","short_pith_number":"pith:N6LEERFV","canonical_record":{"source":{"id":"2403.09167","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-14T08:27:32Z","cross_cats_sorted":[],"title_canon_sha256":"025e89d922cf2fb9327f6619909eae0996bb5205ed7fba514948317392bb01c2","abstract_canon_sha256":"89728fc7e92d592ee6d352c0f599769275b29602081aa9b61c0a98627d0f7d38"},"schema_version":"1.0"},"canonical_sha256":"6f964244b5bed7f45a3c4babed25baa9225075c39342dc50224bb598cecac5fd","source":{"kind":"arxiv","id":"2403.09167","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09167","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09167v1","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09167","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"pith_short_12","alias_value":"N6LEERFVX3L7","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"pith_short_16","alias_value":"N6LEERFVX3L7IWR4","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"pith_short_8","alias_value":"N6LEERFV","created_at":"2026-07-05T07:56:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:N6LEERFVX3L7IWR4JOV62JN2VE","target":"record","payload":{"canonical_record":{"source":{"id":"2403.09167","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-14T08:27:32Z","cross_cats_sorted":[],"title_canon_sha256":"025e89d922cf2fb9327f6619909eae0996bb5205ed7fba514948317392bb01c2","abstract_canon_sha256":"89728fc7e92d592ee6d352c0f599769275b29602081aa9b61c0a98627d0f7d38"},"schema_version":"1.0"},"canonical_sha256":"6f964244b5bed7f45a3c4babed25baa9225075c39342dc50224bb598cecac5fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:56:06.293944Z","signature_b64":"d5MYK+Kd+yE2eOhZyfYfjuqU8dIrzGuX92ArVe+Oc0KrpEBzhoLmLNcTDY+5e7gHy86GIdbHewYSU2YYVbj4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f964244b5bed7f45a3c4babed25baa9225075c39342dc50224bb598cecac5fd","last_reissued_at":"2026-07-05T07:56:06.293486Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:56:06.293486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.09167","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:56:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vlvyp+cyKvQ+bfL/6YWewLHA17pLdsQUXGhCBL1lzTNwj5SQQY0eHQQrWBKYm2sCWJLX1ruL9iKH5rWRotlcAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T07:44:15.490170Z"},"content_sha256":"1c7e0d91d518adecd719269fd7da6fd9c93d7eb070a2c8dceaeb43ed3db0dd06","schema_version":"1.0","event_id":"sha256:1c7e0d91d518adecd719269fd7da6fd9c93d7eb070a2c8dceaeb43ed3db0dd06"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:N6LEERFVX3L7IWR4JOV62JN2VE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Chaoyang Mei, Jianwei Sun, Kaiyu Zheng, Linlin Wei, Ming Cui, Na Liu, Tianyi Li","submitted_at":"2024-03-14T08:27:32Z","abstract_excerpt":"The efficacy of large language models (LLMs) is heavily dependent on the quality of the underlying data, particularly within specialized domains. A common challenge when fine-tuning LLMs for domain-specific applications is the potential degradation of the model's generalization capabilities. To address these issues, we propose a two-stage approach for the construction of production prompts designed to yield high-quality data. This method involves the generation of a diverse array of prompts that encompass a broad spectrum of tasks and exhibit a rich variety of expressions. Furthermore, we intr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09167","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/2403.09167/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:56:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ego1XXlPSEnTyq6o14Xiool+5xXS/u3bBt9kdW3KjYIFLSJwFc2LwQuje5049+ikm5oVjfbRys7XGCbsDBBGBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T07:44:15.490894Z"},"content_sha256":"8c9761d356e4e292247d0a28475ad878fc152cfcd8cfeb3570e42b30cd8ef5ae","schema_version":"1.0","event_id":"sha256:8c9761d356e4e292247d0a28475ad878fc152cfcd8cfeb3570e42b30cd8ef5ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N6LEERFVX3L7IWR4JOV62JN2VE/bundle.json","state_url":"https://pith.science/pith/N6LEERFVX3L7IWR4JOV62JN2VE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N6LEERFVX3L7IWR4JOV62JN2VE/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T07:44:15Z","links":{"resolver":"https://pith.science/pith/N6LEERFVX3L7IWR4JOV62JN2VE","bundle":"https://pith.science/pith/N6LEERFVX3L7IWR4JOV62JN2VE/bundle.json","state":"https://pith.science/pith/N6LEERFVX3L7IWR4JOV62JN2VE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N6LEERFVX3L7IWR4JOV62JN2VE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:N6LEERFVX3L7IWR4JOV62JN2VE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"89728fc7e92d592ee6d352c0f599769275b29602081aa9b61c0a98627d0f7d38","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-14T08:27:32Z","title_canon_sha256":"025e89d922cf2fb9327f6619909eae0996bb5205ed7fba514948317392bb01c2"},"schema_version":"1.0","source":{"id":"2403.09167","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.09167","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"arxiv_version","alias_value":"2403.09167v1","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.09167","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"pith_short_12","alias_value":"N6LEERFVX3L7","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"pith_short_16","alias_value":"N6LEERFVX3L7IWR4","created_at":"2026-07-05T07:56:06Z"},{"alias_kind":"pith_short_8","alias_value":"N6LEERFV","created_at":"2026-07-05T07:56:06Z"}],"graph_snapshots":[{"event_id":"sha256:8c9761d356e4e292247d0a28475ad878fc152cfcd8cfeb3570e42b30cd8ef5ae","target":"graph","created_at":"2026-07-05T07:56:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2403.09167/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The efficacy of large language models (LLMs) is heavily dependent on the quality of the underlying data, particularly within specialized domains. A common challenge when fine-tuning LLMs for domain-specific applications is the potential degradation of the model's generalization capabilities. To address these issues, we propose a two-stage approach for the construction of production prompts designed to yield high-quality data. This method involves the generation of a diverse array of prompts that encompass a broad spectrum of tasks and exhibit a rich variety of expressions. Furthermore, we intr","authors_text":"Chaoyang Mei, Jianwei Sun, Kaiyu Zheng, Linlin Wei, Ming Cui, Na Liu, Tianyi Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-14T08:27:32Z","title":"Dial-insight: Fine-tuning Large Language Models with High-Quality Domain-Specific Data Preventing Capability Collapse"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.09167","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1c7e0d91d518adecd719269fd7da6fd9c93d7eb070a2c8dceaeb43ed3db0dd06","target":"record","created_at":"2026-07-05T07:56:06Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"89728fc7e92d592ee6d352c0f599769275b29602081aa9b61c0a98627d0f7d38","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-14T08:27:32Z","title_canon_sha256":"025e89d922cf2fb9327f6619909eae0996bb5205ed7fba514948317392bb01c2"},"schema_version":"1.0","source":{"id":"2403.09167","kind":"arxiv","version":1}},"canonical_sha256":"6f964244b5bed7f45a3c4babed25baa9225075c39342dc50224bb598cecac5fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f964244b5bed7f45a3c4babed25baa9225075c39342dc50224bb598cecac5fd","first_computed_at":"2026-07-05T07:56:06.293486Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:56:06.293486Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"d5MYK+Kd+yE2eOhZyfYfjuqU8dIrzGuX92ArVe+Oc0KrpEBzhoLmLNcTDY+5e7gHy86GIdbHewYSU2YYVbj4DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:56:06.293944Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.09167","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c7e0d91d518adecd719269fd7da6fd9c93d7eb070a2c8dceaeb43ed3db0dd06","sha256:8c9761d356e4e292247d0a28475ad878fc152cfcd8cfeb3570e42b30cd8ef5ae"],"state_sha256":"a475b102b4b7b7e867fa00dc4c79e5716cc7231a2a1262376ce494d3e4936073"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5YONVoc31UXpmAtK/Pma55ubZthJj6mWpBakDMQS8m4hOuYw3FwlHxYu1ecrpmRI208LX6/rQFcEt50m36OAAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T07:44:15.495891Z","bundle_sha256":"ec5aae8b2dc6e82aed6fb8524baaff9d2812cb29278fea080b336705a8127a8f"}}