{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:L67IFIGQRCPIE6A27MR4VAN3W5","short_pith_number":"pith:L67IFIGQ","schema_version":"1.0","canonical_sha256":"5fbe82a0d0889e82781afb23ca81bbb76a721ab2aae8bdc643cdf35791230f22","source":{"kind":"arxiv","id":"2211.03154","version":1},"attestation_state":"computed","paper":{"title":"On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Han Yu, Xu Guo","submitted_at":"2022-11-06T15:32:00Z","abstract_excerpt":"Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). These PLMs have brought significant performance gains for a range of NLP tasks, circumventing the need to customize complex designs for specific tasks. However, most current work focus on finetuning PLMs on a domain-specific datasets, ignoring the fact that the domain gap can lead to overfitting and even performance drop. Therefore, it is practically important to find an appropriate method to effectively adapt PLMs to a target domain of interest. Recently, a range of methods have been proposed to ac"},"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":"2211.03154","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-11-06T15:32:00Z","cross_cats_sorted":[],"title_canon_sha256":"921b8c16f09c7f3b415465c76c7be2a83aa597ed06e3256c3d2a8bd3b817fad5","abstract_canon_sha256":"f7c77e653f5c55ce702aa522087ea434954e6a5ba630d207eb193bb2cfddd622"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:13:45.153675Z","signature_b64":"yUVy4ap5+hC2POcfY6qHUpNXEHpAiX1RM4GvZtLjWlxY9zRI93Sa8HV96wez2L/tqfHGCWsszAqybeKDLxebCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fbe82a0d0889e82781afb23ca81bbb76a721ab2aae8bdc643cdf35791230f22","last_reissued_at":"2026-07-05T05:13:45.153266Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:13:45.153266Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On the Domain Adaptation and Generalization of Pretrained Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Han Yu, Xu Guo","submitted_at":"2022-11-06T15:32:00Z","abstract_excerpt":"Recent advances in NLP are brought by a range of large-scale pretrained language models (PLMs). These PLMs have brought significant performance gains for a range of NLP tasks, circumventing the need to customize complex designs for specific tasks. However, most current work focus on finetuning PLMs on a domain-specific datasets, ignoring the fact that the domain gap can lead to overfitting and even performance drop. Therefore, it is practically important to find an appropriate method to effectively adapt PLMs to a target domain of interest. Recently, a range of methods have been proposed to ac"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.03154","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/2211.03154/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":"2211.03154","created_at":"2026-07-05T05:13:45.153334+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.03154v1","created_at":"2026-07-05T05:13:45.153334+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.03154","created_at":"2026-07-05T05:13:45.153334+00:00"},{"alias_kind":"pith_short_12","alias_value":"L67IFIGQRCPI","created_at":"2026-07-05T05:13:45.153334+00:00"},{"alias_kind":"pith_short_16","alias_value":"L67IFIGQRCPIE6A2","created_at":"2026-07-05T05:13:45.153334+00:00"},{"alias_kind":"pith_short_8","alias_value":"L67IFIGQ","created_at":"2026-07-05T05:13:45.153334+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04982","citing_title":"Optimizing Small Transformer-Based Language Models for Multi-Label Sentiment Analysis in Short Texts","ref_index":5,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5","json":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5.json","graph_json":"https://pith.science/api/pith-number/L67IFIGQRCPIE6A27MR4VAN3W5/graph.json","events_json":"https://pith.science/api/pith-number/L67IFIGQRCPIE6A27MR4VAN3W5/events.json","paper":"https://pith.science/paper/L67IFIGQ"},"agent_actions":{"view_html":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5","download_json":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5.json","view_paper":"https://pith.science/paper/L67IFIGQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.03154&json=true","fetch_graph":"https://pith.science/api/pith-number/L67IFIGQRCPIE6A27MR4VAN3W5/graph.json","fetch_events":"https://pith.science/api/pith-number/L67IFIGQRCPIE6A27MR4VAN3W5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5/action/storage_attestation","attest_author":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5/action/author_attestation","sign_citation":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5/action/citation_signature","submit_replication":"https://pith.science/pith/L67IFIGQRCPIE6A27MR4VAN3W5/action/replication_record"}},"created_at":"2026-07-05T05:13:45.153334+00:00","updated_at":"2026-07-05T05:13:45.153334+00:00"}