{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:XLMJAUZ7USXWLNYLATHQTVYWWD","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":"cd39e0db71b4ab3c8a5a04e73e95e08aa45cb1b6a5bf5a6210450d8ba6015a19","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-12-22T05:04:41Z","title_canon_sha256":"fd4a50b3dcd869434d9d99a555f68c0a91bfccf1ec92771eea44dc8be47c4412"},"schema_version":"1.0","source":{"id":"2112.11668","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.11668","created_at":"2026-07-05T03:43:09Z"},{"alias_kind":"arxiv_version","alias_value":"2112.11668v1","created_at":"2026-07-05T03:43:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.11668","created_at":"2026-07-05T03:43:09Z"},{"alias_kind":"pith_short_12","alias_value":"XLMJAUZ7USXW","created_at":"2026-07-05T03:43:09Z"},{"alias_kind":"pith_short_16","alias_value":"XLMJAUZ7USXWLNYL","created_at":"2026-07-05T03:43:09Z"},{"alias_kind":"pith_short_8","alias_value":"XLMJAUZ7","created_at":"2026-07-05T03:43:09Z"}],"graph_snapshots":[{"event_id":"sha256:598ff3c0b3aa3124866efc1022011bedfad2a38c766c5f4340a5627942a92802","target":"graph","created_at":"2026-07-05T03:43:09Z","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/2112.11668/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet, it is strikingly vulnerable to adversarial examples, e.g., word substitution attacks using only synonyms can easily fool a BERT-based sentiment analysis model. In this paper, we demonstrate that adversarial training, the prevalent defense technique, does not directly fit a conventional fine-tuning scenario, because it suffers severely from catastrophic forgetting: failing to retain the generic and robust linguistic features that have already been captured by the pre-trained model. In this light, we prop","authors_text":"Hanwang Zhang, Luu Anh Tuan, Min Lin, Shuicheng Yan, Xinhsuai Dong","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-12-22T05:04:41Z","title":"How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.11668","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":"sha256:671f10d4af31a9c9e901f34b34d11aac156fe92fa93517bd790d287854ac8f9b","target":"record","created_at":"2026-07-05T03:43:09Z","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":"cd39e0db71b4ab3c8a5a04e73e95e08aa45cb1b6a5bf5a6210450d8ba6015a19","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-12-22T05:04:41Z","title_canon_sha256":"fd4a50b3dcd869434d9d99a555f68c0a91bfccf1ec92771eea44dc8be47c4412"},"schema_version":"1.0","source":{"id":"2112.11668","kind":"arxiv","version":1}},"canonical_sha256":"bad890533fa4af65b70b04cf09d716b0d257790395667acc1b00a13413820f66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bad890533fa4af65b70b04cf09d716b0d257790395667acc1b00a13413820f66","first_computed_at":"2026-07-05T03:43:09.483149Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:43:09.483149Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Dh7gFY2Pi0N9PEKnJLfMDdKiaqC8KHYX+8AfoGLkMm2aDlzjteuC7k4N5H7T5sux/4GYdKGhgADzGyns4XAKBg==","signature_status":"signed_v1","signed_at":"2026-07-05T03:43:09.483498Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.11668","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:671f10d4af31a9c9e901f34b34d11aac156fe92fa93517bd790d287854ac8f9b","sha256:598ff3c0b3aa3124866efc1022011bedfad2a38c766c5f4340a5627942a92802"],"state_sha256":"dfe3dc74dcb9d6c6ba4165ff1eb118aff2c1e68de2dc8f003f1a81d484719a02"}