{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:MVJXYQYBCQBZEOAIYNUM2YSTAQ","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":"d94367dc643e24c0f131542b80778fb846c1a39a35fe04c4fac006a07c2a4595","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-06-11T12:12:20Z","title_canon_sha256":"e393d0993690ad68026cd80f914db2bb4473923ec33b9d1ea5ff31213e775f4a"},"schema_version":"1.0","source":{"id":"2206.05511","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2206.05511","created_at":"2026-07-05T04:31:00Z"},{"alias_kind":"arxiv_version","alias_value":"2206.05511v1","created_at":"2026-07-05T04:31:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.05511","created_at":"2026-07-05T04:31:00Z"},{"alias_kind":"pith_short_12","alias_value":"MVJXYQYBCQBZ","created_at":"2026-07-05T04:31:00Z"},{"alias_kind":"pith_short_16","alias_value":"MVJXYQYBCQBZEOAI","created_at":"2026-07-05T04:31:00Z"},{"alias_kind":"pith_short_8","alias_value":"MVJXYQYB","created_at":"2026-07-05T04:31:00Z"}],"graph_snapshots":[{"event_id":"sha256:e2c9d0cb90125032b02a4c9fb2c6acd50342d28af94cbdb56f11a0c0ac7e417b","target":"graph","created_at":"2026-07-05T04:31:00Z","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/2206.05511/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Existing studies have demonstrated that adversarial examples can be directly attributed to the presence of non-robust features, which are highly predictive, but can be easily manipulated by adversaries to fool NLP models. In this study, we explore the feasibility of capturing task-specific robust features, while eliminating the non-robust ones by using the information bottleneck theory. Through extensive experiments, we show that the models trained with our information bottleneck-based method are able to achieve a significant improvement in robust accuracy, exceeding performances of all the pr","authors_text":"Cenyuan Zhang, Cho-Jui Hsieh, Kai-Wei Chang, Xiang Zhou, Xiaoqing Zheng, Yixin Wan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-06-11T12:12:20Z","title":"Improving the Adversarial Robustness of NLP Models by Information Bottleneck"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.05511","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:f27cb4cb5d9bdb9bcd7a6163d44db0a29ae4c1ecdb7785cd9bdaa7e8c64a8abc","target":"record","created_at":"2026-07-05T04:31:00Z","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":"d94367dc643e24c0f131542b80778fb846c1a39a35fe04c4fac006a07c2a4595","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-06-11T12:12:20Z","title_canon_sha256":"e393d0993690ad68026cd80f914db2bb4473923ec33b9d1ea5ff31213e775f4a"},"schema_version":"1.0","source":{"id":"2206.05511","kind":"arxiv","version":1}},"canonical_sha256":"65537c43011403923808c368cd6253041f3f9447726a722166ad871c45da7abc","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"65537c43011403923808c368cd6253041f3f9447726a722166ad871c45da7abc","first_computed_at":"2026-07-05T04:31:00.134550Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:31:00.134550Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fGJP47wjf5wAuU0BLYZvwuDTDQ9rnjwFKKouvI4snnZJ9RIlv2WoMD+A9QSn9wPwt7DytAOLNYwVJA2swPJiDw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:31:00.134942Z","signed_message":"canonical_sha256_bytes"},"source_id":"2206.05511","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f27cb4cb5d9bdb9bcd7a6163d44db0a29ae4c1ecdb7785cd9bdaa7e8c64a8abc","sha256:e2c9d0cb90125032b02a4c9fb2c6acd50342d28af94cbdb56f11a0c0ac7e417b"],"state_sha256":"0639190c3024842728e441e2268653cbc16299e2f996a925a9ca14caa6316033"}