{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:L3CSYG54W7UZ5WPR6GOO623EZP","short_pith_number":"pith:L3CSYG54","canonical_record":{"source":{"id":"2405.10767","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-05-17T13:27:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e09fa884107d1ef3d416ee9d06708780b5148e22ac0e46589b706f820cc5a213","abstract_canon_sha256":"51bb7fb5ca961774fb9d98efc99cbbdfc8ae9846b613e32fe134a38a046fc0ca"},"schema_version":"1.0"},"canonical_sha256":"5ec52c1bbcb7e99ed9f1f19cef6b64cbeafe41d3949951d98ed6c38b2896a440","source":{"kind":"arxiv","id":"2405.10767","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.10767","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"arxiv_version","alias_value":"2405.10767v1","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10767","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"pith_short_12","alias_value":"L3CSYG54W7UZ","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"pith_short_16","alias_value":"L3CSYG54W7UZ5WPR","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"pith_short_8","alias_value":"L3CSYG54","created_at":"2026-07-05T08:20:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:L3CSYG54W7UZ5WPR6GOO623EZP","target":"record","payload":{"canonical_record":{"source":{"id":"2405.10767","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-05-17T13:27:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e09fa884107d1ef3d416ee9d06708780b5148e22ac0e46589b706f820cc5a213","abstract_canon_sha256":"51bb7fb5ca961774fb9d98efc99cbbdfc8ae9846b613e32fe134a38a046fc0ca"},"schema_version":"1.0"},"canonical_sha256":"5ec52c1bbcb7e99ed9f1f19cef6b64cbeafe41d3949951d98ed6c38b2896a440","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:20:16.719516Z","signature_b64":"VsGBG5d8JTipSNzwwsCJMRp7UcCqNWJeK+C8Itjzke+OSQ7/oiv5cqxbta6oHEKYJyKf5JZZZmpN72CYB2NPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5ec52c1bbcb7e99ed9f1f19cef6b64cbeafe41d3949951d98ed6c38b2896a440","last_reissued_at":"2026-07-05T08:20:16.719030Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:20:16.719030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.10767","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":"2026-07-05T08:20:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qG2OadIQ4ZcwQpHB6R6C1XYPhypicBOwjxOUEddApCE2RDb5flUzblb2o0PHwHoT6n83Liilwe87KC7inCcNBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:51:22.869628Z"},"content_sha256":"6c9d534ee8af3fe2c4becc24b38770c76024725d816b920c8bd58db30ac5bcff","schema_version":"1.0","event_id":"sha256:6c9d534ee8af3fe2c4becc24b38770c76024725d816b920c8bd58db30ac5bcff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:L3CSYG54W7UZ5WPR6GOO623EZP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Evaluating Saliency Explanations in NLP by Crowdsourcing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.HC","authors_text":"Hisashi Kashima, Jiyi Li, Koh Takeuchi, Xiaofeng Lin, Xiaotian Lu, Zhen Wan","submitted_at":"2024-05-17T13:27:45Z","abstract_excerpt":"Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications. Various saliency explanation methods, which give each feature of input a score proportional to the contribution of output, have been proposed to determine the part of the input which a model values most. Despite a considerable body of work on the evaluation of saliency methods, whether the results of various ev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10767","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/2405.10767/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":"2026-07-05T08:20:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sdOjA4sEGNxob3Ud10bbIMGlMu4Ql1jPMP3Leut7qqK0FLHNA/mVt0uZKw7PRICj3PQ+CTZVSOSR6AQLiA8RCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T03:51:22.870005Z"},"content_sha256":"ad4e00986a463a93b8fffa35e66db9849b4b9c814f2e6e38419998afcdcb9ba7","schema_version":"1.0","event_id":"sha256:ad4e00986a463a93b8fffa35e66db9849b4b9c814f2e6e38419998afcdcb9ba7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/L3CSYG54W7UZ5WPR6GOO623EZP/bundle.json","state_url":"https://pith.science/pith/L3CSYG54W7UZ5WPR6GOO623EZP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/L3CSYG54W7UZ5WPR6GOO623EZP/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":"2026-07-07T03:51:22Z","links":{"resolver":"https://pith.science/pith/L3CSYG54W7UZ5WPR6GOO623EZP","bundle":"https://pith.science/pith/L3CSYG54W7UZ5WPR6GOO623EZP/bundle.json","state":"https://pith.science/pith/L3CSYG54W7UZ5WPR6GOO623EZP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/L3CSYG54W7UZ5WPR6GOO623EZP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:L3CSYG54W7UZ5WPR6GOO623EZP","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":"51bb7fb5ca961774fb9d98efc99cbbdfc8ae9846b613e32fe134a38a046fc0ca","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-05-17T13:27:45Z","title_canon_sha256":"e09fa884107d1ef3d416ee9d06708780b5148e22ac0e46589b706f820cc5a213"},"schema_version":"1.0","source":{"id":"2405.10767","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.10767","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"arxiv_version","alias_value":"2405.10767v1","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.10767","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"pith_short_12","alias_value":"L3CSYG54W7UZ","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"pith_short_16","alias_value":"L3CSYG54W7UZ5WPR","created_at":"2026-07-05T08:20:16Z"},{"alias_kind":"pith_short_8","alias_value":"L3CSYG54","created_at":"2026-07-05T08:20:16Z"}],"graph_snapshots":[{"event_id":"sha256:ad4e00986a463a93b8fffa35e66db9849b4b9c814f2e6e38419998afcdcb9ba7","target":"graph","created_at":"2026-07-05T08:20:16Z","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/2405.10767/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep learning models have performed well on many NLP tasks. However, their internal mechanisms are typically difficult for humans to understand. The development of methods to explain models has become a key issue in the reliability of deep learning models in many important applications. Various saliency explanation methods, which give each feature of input a score proportional to the contribution of output, have been proposed to determine the part of the input which a model values most. Despite a considerable body of work on the evaluation of saliency methods, whether the results of various ev","authors_text":"Hisashi Kashima, Jiyi Li, Koh Takeuchi, Xiaofeng Lin, Xiaotian Lu, Zhen Wan","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-05-17T13:27:45Z","title":"Evaluating Saliency Explanations in NLP by Crowdsourcing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.10767","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":"sha256:6c9d534ee8af3fe2c4becc24b38770c76024725d816b920c8bd58db30ac5bcff","target":"record","created_at":"2026-07-05T08:20:16Z","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":"51bb7fb5ca961774fb9d98efc99cbbdfc8ae9846b613e32fe134a38a046fc0ca","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.HC","submitted_at":"2024-05-17T13:27:45Z","title_canon_sha256":"e09fa884107d1ef3d416ee9d06708780b5148e22ac0e46589b706f820cc5a213"},"schema_version":"1.0","source":{"id":"2405.10767","kind":"arxiv","version":1}},"canonical_sha256":"5ec52c1bbcb7e99ed9f1f19cef6b64cbeafe41d3949951d98ed6c38b2896a440","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5ec52c1bbcb7e99ed9f1f19cef6b64cbeafe41d3949951d98ed6c38b2896a440","first_computed_at":"2026-07-05T08:20:16.719030Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:20:16.719030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"VsGBG5d8JTipSNzwwsCJMRp7UcCqNWJeK+C8Itjzke+OSQ7/oiv5cqxbta6oHEKYJyKf5JZZZmpN72CYB2NPAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:20:16.719516Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.10767","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c9d534ee8af3fe2c4becc24b38770c76024725d816b920c8bd58db30ac5bcff","sha256:ad4e00986a463a93b8fffa35e66db9849b4b9c814f2e6e38419998afcdcb9ba7"],"state_sha256":"97594f39515c6dfc3ae46534f60f2763bfa8a8e61910dbef9a28fa702c2bcda3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B119BV3w5YzQbfChqJwTIS2eEPerxXtJYPPCOAl3IRZI2hOGibxRvHHLO3+q3dJI1dJDdrqbP0HzAu1UexYTAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T03:51:22.872028Z","bundle_sha256":"d02854c863f3817044916a6f9d0838f43d137b2593f8ba3317babece9ff23719"}}