{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:6YAQWWVVYEILXDKOTQNYTXHCSJ","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":"c5af98f83c0b5da178b22400ad2e7ea09b441177283194024dc6e86831726f65","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-15T08:12:52Z","title_canon_sha256":"1642851bc1e98e51f017f69b8e3545964ebdd032dbc7e242051b05684ea192c6"},"schema_version":"1.0","source":{"id":"2408.08003","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.08003","created_at":"2026-07-05T08:55:43Z"},{"alias_kind":"arxiv_version","alias_value":"2408.08003v1","created_at":"2026-07-05T08:55:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.08003","created_at":"2026-07-05T08:55:43Z"},{"alias_kind":"pith_short_12","alias_value":"6YAQWWVVYEIL","created_at":"2026-07-05T08:55:43Z"},{"alias_kind":"pith_short_16","alias_value":"6YAQWWVVYEILXDKO","created_at":"2026-07-05T08:55:43Z"},{"alias_kind":"pith_short_8","alias_value":"6YAQWWVV","created_at":"2026-07-05T08:55:43Z"}],"graph_snapshots":[{"event_id":"sha256:37c3d814dd1ccd07c348c10eee2c755f87e415d531374b1353da1a201f12b613","target":"graph","created_at":"2026-07-05T08:55:43Z","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/2408.08003/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Most large language models are fine-tuned using either expensive human-annotated data or GPT-4 generated data which cannot guarantee performance in certain domains. We argue that although the web-crawled data often has formatting errors causing semantic inaccuracies, it can still serve as a valuable source for high-quality supervised fine-tuning in specific domains without relying on advanced models like GPT-4. To this end, we create a paired training dataset automatically by aligning web-crawled data with a smaller set of high-quality data. By training a language model on this dataset, we can","authors_text":"Chenglin Jiang, Jing Zhou, Wei Shen, Xiaonan He, Xiao Zhou","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-15T08:12:52Z","title":"Leveraging Web-Crawled Data for High-Quality Fine-Tuning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.08003","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:e587296dddf34a66c2599f04aa34b8ca35d02939d8af75977fce91555a141a0b","target":"record","created_at":"2026-07-05T08:55:43Z","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":"c5af98f83c0b5da178b22400ad2e7ea09b441177283194024dc6e86831726f65","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-08-15T08:12:52Z","title_canon_sha256":"1642851bc1e98e51f017f69b8e3545964ebdd032dbc7e242051b05684ea192c6"},"schema_version":"1.0","source":{"id":"2408.08003","kind":"arxiv","version":1}},"canonical_sha256":"f6010b5ab5c110bb8d4e9c1b89dce29246f867cdf69f4339e508874854810bf8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f6010b5ab5c110bb8d4e9c1b89dce29246f867cdf69f4339e508874854810bf8","first_computed_at":"2026-07-05T08:55:43.110427Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:55:43.110427Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PzuLnexQJtnvo8dupo9wIw4vDDqT1pEdKt78xf51IsZiQMgybGB78cYw4cuIaAfK/4QShzchOd9p0XlE4HeNCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:55:43.110769Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.08003","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e587296dddf34a66c2599f04aa34b8ca35d02939d8af75977fce91555a141a0b","sha256:37c3d814dd1ccd07c348c10eee2c755f87e415d531374b1353da1a201f12b613"],"state_sha256":"84233eb67cbaf9a34ccfd923974859021397f60daeaa00c4b56b84eabf2ca55f"}