{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RWABESMPK2RPJ25ETRFXAHNUD7","short_pith_number":"pith:RWABESMP","canonical_record":{"source":{"id":"2310.03328","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-05T05:55:06Z","cross_cats_sorted":[],"title_canon_sha256":"5cc55f51799dffeb4e15e8f9d494cd375cfde94bf014c01b244c477ac02dcd18","abstract_canon_sha256":"3cb2a59f1529d94922f83e482bc7277fdd116dc10068b99bd630480ff16c7382"},"schema_version":"1.0"},"canonical_sha256":"8d8012498f56a2f4eba49c4b701db41fc6b824b0eddcff37f9a39ad4242eac73","source":{"kind":"arxiv","id":"2310.03328","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.03328","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"arxiv_version","alias_value":"2310.03328v3","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.03328","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"pith_short_12","alias_value":"RWABESMPK2RP","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"pith_short_16","alias_value":"RWABESMPK2RPJ25E","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"pith_short_8","alias_value":"RWABESMP","created_at":"2026-07-05T08:59:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RWABESMPK2RPJ25ETRFXAHNUD7","target":"record","payload":{"canonical_record":{"source":{"id":"2310.03328","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-05T05:55:06Z","cross_cats_sorted":[],"title_canon_sha256":"5cc55f51799dffeb4e15e8f9d494cd375cfde94bf014c01b244c477ac02dcd18","abstract_canon_sha256":"3cb2a59f1529d94922f83e482bc7277fdd116dc10068b99bd630480ff16c7382"},"schema_version":"1.0"},"canonical_sha256":"8d8012498f56a2f4eba49c4b701db41fc6b824b0eddcff37f9a39ad4242eac73","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:59:03.960798Z","signature_b64":"BW/lCYZ1BP5AHAw7XoNekXrD0Mgx9vL/Yy3+Hwe0u/swcVVTgsETqg8sX0az/8YqTdvoLWPSkVsuDUiWGMVZBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d8012498f56a2f4eba49c4b701db41fc6b824b0eddcff37f9a39ad4242eac73","last_reissued_at":"2026-07-05T08:59:03.960341Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:59:03.960341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.03328","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:59:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AaQzceVSbVmqXIdfbYq19zn4ZVC4XYaocbzUpy7qs33wtRlO/EzJPURvnru8eIDTtmgGqbXlveDK5Cz0RvgNDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:07:48.108573Z"},"content_sha256":"df9a025dc3575557b3d01e4ded416cee27557edf12333eb7631b9cbf70caef85","schema_version":"1.0","event_id":"sha256:df9a025dc3575557b3d01e4ded416cee27557edf12333eb7631b9cbf70caef85"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RWABESMPK2RPJ25ETRFXAHNUD7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fei Cheng, Sadao Kurohashi, Yating Zhang, Yexiang Wang, Zhen Wan","submitted_at":"2023-10-05T05:55:06Z","abstract_excerpt":"While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capabilities in general domain tasks, they often generate content with hallucinations in specific domains such as Chinese law, hindering their application in these areas. This is typically due to the absence of training data that encompasses such a specific domain, preventing GPT-4 from acquiring in-domain knowledge. A pressing challenge is that it's not plausible to continue training LLMs of such scale on in-domain data.\n  This paper introduces a simple and effective domain adaptation framework for "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.03328","kind":"arxiv","version":3},"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/2310.03328/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":"2026-07-05T08:59:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"f+qBfoMN8sIs1ElRbjW9VSwRFgRG81z6TEYZ0aix2FNyHGyfZA01iENfTtv3/x4RttPUTd+wYTo1VcLCMBfRBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T10:07:48.109121Z"},"content_sha256":"6fb89715a0f88243c24e9e0a4b3c89e5521c086d0996a6d58bef21ab118a4b12","schema_version":"1.0","event_id":"sha256:6fb89715a0f88243c24e9e0a4b3c89e5521c086d0996a6d58bef21ab118a4b12"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RWABESMPK2RPJ25ETRFXAHNUD7/bundle.json","state_url":"https://pith.science/pith/RWABESMPK2RPJ25ETRFXAHNUD7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RWABESMPK2RPJ25ETRFXAHNUD7/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":"2026-08-13T10:07:48Z","links":{"resolver":"https://pith.science/pith/RWABESMPK2RPJ25ETRFXAHNUD7","bundle":"https://pith.science/pith/RWABESMPK2RPJ25ETRFXAHNUD7/bundle.json","state":"https://pith.science/pith/RWABESMPK2RPJ25ETRFXAHNUD7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RWABESMPK2RPJ25ETRFXAHNUD7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RWABESMPK2RPJ25ETRFXAHNUD7","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":"3cb2a59f1529d94922f83e482bc7277fdd116dc10068b99bd630480ff16c7382","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-05T05:55:06Z","title_canon_sha256":"5cc55f51799dffeb4e15e8f9d494cd375cfde94bf014c01b244c477ac02dcd18"},"schema_version":"1.0","source":{"id":"2310.03328","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.03328","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"arxiv_version","alias_value":"2310.03328v3","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.03328","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"pith_short_12","alias_value":"RWABESMPK2RP","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"pith_short_16","alias_value":"RWABESMPK2RPJ25E","created_at":"2026-07-05T08:59:03Z"},{"alias_kind":"pith_short_8","alias_value":"RWABESMP","created_at":"2026-07-05T08:59:03Z"}],"graph_snapshots":[{"event_id":"sha256:6fb89715a0f88243c24e9e0a4b3c89e5521c086d0996a6d58bef21ab118a4b12","target":"graph","created_at":"2026-07-05T08:59:03Z","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/2310.03328/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capabilities in general domain tasks, they often generate content with hallucinations in specific domains such as Chinese law, hindering their application in these areas. This is typically due to the absence of training data that encompasses such a specific domain, preventing GPT-4 from acquiring in-domain knowledge. A pressing challenge is that it's not plausible to continue training LLMs of such scale on in-domain data.\n  This paper introduces a simple and effective domain adaptation framework for ","authors_text":"Fei Cheng, Sadao Kurohashi, Yating Zhang, Yexiang Wang, Zhen Wan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-05T05:55:06Z","title":"Reformulating Domain Adaptation of Large Language Models as Adapt-Retrieve-Revise: A Case Study on Chinese Legal Domain"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.03328","kind":"arxiv","version":3},"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:df9a025dc3575557b3d01e4ded416cee27557edf12333eb7631b9cbf70caef85","target":"record","created_at":"2026-07-05T08:59:03Z","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":"3cb2a59f1529d94922f83e482bc7277fdd116dc10068b99bd630480ff16c7382","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-05T05:55:06Z","title_canon_sha256":"5cc55f51799dffeb4e15e8f9d494cd375cfde94bf014c01b244c477ac02dcd18"},"schema_version":"1.0","source":{"id":"2310.03328","kind":"arxiv","version":3}},"canonical_sha256":"8d8012498f56a2f4eba49c4b701db41fc6b824b0eddcff37f9a39ad4242eac73","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d8012498f56a2f4eba49c4b701db41fc6b824b0eddcff37f9a39ad4242eac73","first_computed_at":"2026-07-05T08:59:03.960341Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:59:03.960341Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BW/lCYZ1BP5AHAw7XoNekXrD0Mgx9vL/Yy3+Hwe0u/swcVVTgsETqg8sX0az/8YqTdvoLWPSkVsuDUiWGMVZBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:59:03.960798Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.03328","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:df9a025dc3575557b3d01e4ded416cee27557edf12333eb7631b9cbf70caef85","sha256:6fb89715a0f88243c24e9e0a4b3c89e5521c086d0996a6d58bef21ab118a4b12"],"state_sha256":"ca3e8cd4bca03c55b53ca54a675b6fa8417910af5281b866e658038d16398d5e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ttiCj+y8i4OBENUvZZdUIPl1XIld+2bRANp1a1Mh3I3CpqnL1KnNKuCyyVr09HzV44w57vbstKrgB5puH8l9AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T10:07:48.181290Z","bundle_sha256":"cfb53c2a04745034a838d7fe10e84e0c9b5dfd87e88d3b8db3ebef861ad28f8f"}}