{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:5ZWFXW5RGGA7WHGTPPC5KGJMIM","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":"83f9f90b031f259d0e84c54dab0f6240544dec47aabf938d8d582e7352218445","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T20:46:43Z","title_canon_sha256":"724852c2ab7b1e64ceddd8aa840fd497ca46c29d1795b0e839cae7d739c1f8cc"},"schema_version":"1.0","source":{"id":"2212.09849","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.09849","created_at":"2026-07-05T11:06:59Z"},{"alias_kind":"arxiv_version","alias_value":"2212.09849v6","created_at":"2026-07-05T11:06:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09849","created_at":"2026-07-05T11:06:59Z"},{"alias_kind":"pith_short_12","alias_value":"5ZWFXW5RGGA7","created_at":"2026-07-05T11:06:59Z"},{"alias_kind":"pith_short_16","alias_value":"5ZWFXW5RGGA7WHGT","created_at":"2026-07-05T11:06:59Z"},{"alias_kind":"pith_short_8","alias_value":"5ZWFXW5R","created_at":"2026-07-05T11:06:59Z"}],"graph_snapshots":[{"event_id":"sha256:18adf13795e9a00e3a4845b8e1ed90a01c6f6133ba409e12e0a5218d1dc4ee2a","target":"graph","created_at":"2026-07-05T11:06:59Z","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/2212.09849/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training data is not, due to data privacy or intellectual property concerns. This creates a barrier to fusing knowledge across individual models to yield a better single model. In this paper, we study the problem of merging individual models built on different training data sets to obtain a single model that performs well both across all data set domains and can generalize on out-of-domain data. We propose a dataless knowledg","authors_text":"Daniel Preotiuc-Pietro, Pengxiang Cheng, Xiang Ren, Xisen Jin","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T20:46:43Z","title":"Dataless Knowledge Fusion by Merging Weights of Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09849","kind":"arxiv","version":6},"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":"sha256:4ab2d8222f15842e2ed393b7c38b77048cfafef3f6bbdd1d72dbca23ca380a8b","target":"record","created_at":"2026-07-05T11:06:59Z","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":"83f9f90b031f259d0e84c54dab0f6240544dec47aabf938d8d582e7352218445","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T20:46:43Z","title_canon_sha256":"724852c2ab7b1e64ceddd8aa840fd497ca46c29d1795b0e839cae7d739c1f8cc"},"schema_version":"1.0","source":{"id":"2212.09849","kind":"arxiv","version":6}},"canonical_sha256":"ee6c5bdbb13181fb1cd37bc5d5192c433624cc3987703324a096607fcd187bef","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ee6c5bdbb13181fb1cd37bc5d5192c433624cc3987703324a096607fcd187bef","first_computed_at":"2026-07-05T11:06:59.464729Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:06:59.464729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HnShSx+7NDH5r7gCrGmMkHXKjb9NBHpG81nySXHBjuG30FMIgWAQf2U0fTu0qQFDWztyV2QcN3juIW5QXfx6Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:06:59.465184Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.09849","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4ab2d8222f15842e2ed393b7c38b77048cfafef3f6bbdd1d72dbca23ca380a8b","sha256:18adf13795e9a00e3a4845b8e1ed90a01c6f6133ba409e12e0a5218d1dc4ee2a"],"state_sha256":"f45364586f30e979821e1e0ff71e21291644ca29233fa67e8ae31a8b9f74bbc3"}