{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:J2PZPC4D3IKD6VMQXVUDIXHSYA","short_pith_number":"pith:J2PZPC4D","canonical_record":{"source":{"id":"2010.02338","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T21:07:45Z","cross_cats_sorted":[],"title_canon_sha256":"9379e9e7dc8f8cca82cb4a9256f48ea40e5ff7a7cce7be40e810fc9dca59dfaa","abstract_canon_sha256":"999fcbd083df4ee58cef89427663ea3f0ea6d8db0bdc3b891bdf5dab82b5bff7"},"schema_version":"1.0"},"canonical_sha256":"4e9f978b83da143f5590bd68345cf2c03119827446e8879d948c72eaba7ee431","source":{"kind":"arxiv","id":"2010.02338","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.02338","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"arxiv_version","alias_value":"2010.02338v1","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02338","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"pith_short_12","alias_value":"J2PZPC4D3IKD","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"pith_short_16","alias_value":"J2PZPC4D3IKD6VMQ","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"pith_short_8","alias_value":"J2PZPC4D","created_at":"2026-07-05T01:40:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:J2PZPC4D3IKD6VMQXVUDIXHSYA","target":"record","payload":{"canonical_record":{"source":{"id":"2010.02338","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T21:07:45Z","cross_cats_sorted":[],"title_canon_sha256":"9379e9e7dc8f8cca82cb4a9256f48ea40e5ff7a7cce7be40e810fc9dca59dfaa","abstract_canon_sha256":"999fcbd083df4ee58cef89427663ea3f0ea6d8db0bdc3b891bdf5dab82b5bff7"},"schema_version":"1.0"},"canonical_sha256":"4e9f978b83da143f5590bd68345cf2c03119827446e8879d948c72eaba7ee431","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:40:48.902583Z","signature_b64":"krnZccBkPUQY62gDVq5yf5OhEvDtXxcB5tr/jCEIZS2fmqnE6sgKyim2FigmGoUUQTN2HIKWvIenGTW/S4/HCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e9f978b83da143f5590bd68345cf2c03119827446e8879d948c72eaba7ee431","last_reissued_at":"2026-07-05T01:40:48.902184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:40:48.902184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2010.02338","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:40:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PNewYrLaELciE33QLV2b/CtuzgAYva+wejDE85tq400qogdexjqvXwwKvezr0D8F0a/OqLHWopUDrq8anc2VDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T10:25:25.273503Z"},"content_sha256":"ba368bb919314e508c26b056ee1dadff67898ef5c852c6e160c9eb5a17231a5c","schema_version":"1.0","event_id":"sha256:ba368bb919314e508c26b056ee1dadff67898ef5c852c6e160c9eb5a17231a5c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:J2PZPC4D3IKD6VMQXVUDIXHSYA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alex Beutel, Ben Packer, Ed Chi, Jilin Chen, Kang Li, Tianlu Wang, Xuezhi Wang, Yao Qin","submitted_at":"2020-10-05T21:07:45Z","abstract_excerpt":"NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP da"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02338","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/2010.02338/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":"2026-07-05T01:40:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JsWLEMemzwcvHA8y5+1WHv29ChgYOSSeValwMXsRx4zMfYF3TEvsPJ2pyICai6clTz9cyYdCNRL2htNAJWP3CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-05T10:25:25.274020Z"},"content_sha256":"5682d59c8938e9475e3f47735984b6ffd3c326a018dae7a22933362239815320","schema_version":"1.0","event_id":"sha256:5682d59c8938e9475e3f47735984b6ffd3c326a018dae7a22933362239815320"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA/bundle.json","state_url":"https://pith.science/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA/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":"2026-07-05T10:25:25Z","links":{"resolver":"https://pith.science/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA","bundle":"https://pith.science/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA/bundle.json","state":"https://pith.science/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J2PZPC4D3IKD6VMQXVUDIXHSYA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:J2PZPC4D3IKD6VMQXVUDIXHSYA","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":"999fcbd083df4ee58cef89427663ea3f0ea6d8db0bdc3b891bdf5dab82b5bff7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T21:07:45Z","title_canon_sha256":"9379e9e7dc8f8cca82cb4a9256f48ea40e5ff7a7cce7be40e810fc9dca59dfaa"},"schema_version":"1.0","source":{"id":"2010.02338","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2010.02338","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"arxiv_version","alias_value":"2010.02338v1","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.02338","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"pith_short_12","alias_value":"J2PZPC4D3IKD","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"pith_short_16","alias_value":"J2PZPC4D3IKD6VMQ","created_at":"2026-07-05T01:40:48Z"},{"alias_kind":"pith_short_8","alias_value":"J2PZPC4D","created_at":"2026-07-05T01:40:48Z"}],"graph_snapshots":[{"event_id":"sha256:5682d59c8938e9475e3f47735984b6ffd3c326a018dae7a22933362239815320","target":"graph","created_at":"2026-07-05T01:40:48Z","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/2010.02338/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"NLP models are shown to suffer from robustness issues, i.e., a model's prediction can be easily changed under small perturbations to the input. In this work, we present a Controlled Adversarial Text Generation (CAT-Gen) model that, given an input text, generates adversarial texts through controllable attributes that are known to be invariant to task labels. For example, in order to attack a model for sentiment classification over product reviews, we can use the product categories as the controllable attribute which would not change the sentiment of the reviews. Experiments on real-world NLP da","authors_text":"Alex Beutel, Ben Packer, Ed Chi, Jilin Chen, Kang Li, Tianlu Wang, Xuezhi Wang, Yao Qin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T21:07:45Z","title":"CAT-Gen: Improving Robustness in NLP Models via Controlled Adversarial Text Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.02338","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:ba368bb919314e508c26b056ee1dadff67898ef5c852c6e160c9eb5a17231a5c","target":"record","created_at":"2026-07-05T01:40:48Z","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":"999fcbd083df4ee58cef89427663ea3f0ea6d8db0bdc3b891bdf5dab82b5bff7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2020-10-05T21:07:45Z","title_canon_sha256":"9379e9e7dc8f8cca82cb4a9256f48ea40e5ff7a7cce7be40e810fc9dca59dfaa"},"schema_version":"1.0","source":{"id":"2010.02338","kind":"arxiv","version":1}},"canonical_sha256":"4e9f978b83da143f5590bd68345cf2c03119827446e8879d948c72eaba7ee431","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e9f978b83da143f5590bd68345cf2c03119827446e8879d948c72eaba7ee431","first_computed_at":"2026-07-05T01:40:48.902184Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:40:48.902184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"krnZccBkPUQY62gDVq5yf5OhEvDtXxcB5tr/jCEIZS2fmqnE6sgKyim2FigmGoUUQTN2HIKWvIenGTW/S4/HCg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:40:48.902583Z","signed_message":"canonical_sha256_bytes"},"source_id":"2010.02338","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba368bb919314e508c26b056ee1dadff67898ef5c852c6e160c9eb5a17231a5c","sha256:5682d59c8938e9475e3f47735984b6ffd3c326a018dae7a22933362239815320"],"state_sha256":"d3d200d33c199d23013d83af012b586ca31d8ed41ccee0fb349531be5d198b2c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"glwox13mSxoCOLfjX6OmKoihFeUnCJWfVDdIMpGuqkROXRUAn7KF0oTQL5n3PX7d00MfjL02UcsEQgPqeJXqDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-05T10:25:25.277384Z","bundle_sha256":"0f67e75e94ba8d324e683a8469222bd99c0e05471ecbe4779e9abf38e8b2c314"}}