{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:AQ7I6NLGTOJTYNNEVMKTAD64KD","short_pith_number":"pith:AQ7I6NLG","canonical_record":{"source":{"id":"2307.01503","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-04T06:23:04Z","cross_cats_sorted":[],"title_canon_sha256":"6fad14874fb4737b7180602ba8fe592568dcf53f6aaeb1916410b7514d951267","abstract_canon_sha256":"501efa12f881c670e03f7cbe39d973069bf760781d6fc6c23d558005c18c80a1"},"schema_version":"1.0"},"canonical_sha256":"043e8f35669b933c35a4ab15300fdc50cb3d328667ee1f1d821d4d840dc22a76","source":{"kind":"arxiv","id":"2307.01503","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.01503","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"arxiv_version","alias_value":"2307.01503v1","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01503","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"pith_short_12","alias_value":"AQ7I6NLGTOJT","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"AQ7I6NLGTOJTYNNE","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"AQ7I6NLG","created_at":"2026-07-05T06:27:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:AQ7I6NLGTOJTYNNEVMKTAD64KD","target":"record","payload":{"canonical_record":{"source":{"id":"2307.01503","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-04T06:23:04Z","cross_cats_sorted":[],"title_canon_sha256":"6fad14874fb4737b7180602ba8fe592568dcf53f6aaeb1916410b7514d951267","abstract_canon_sha256":"501efa12f881c670e03f7cbe39d973069bf760781d6fc6c23d558005c18c80a1"},"schema_version":"1.0"},"canonical_sha256":"043e8f35669b933c35a4ab15300fdc50cb3d328667ee1f1d821d4d840dc22a76","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:27:57.390075Z","signature_b64":"afnQecL1EJZZPQ73dWJL+9Tl4ncCMYuBFIj1R6GlFjPORG1fTbqfvZjOjtm7GMKAAY42XwV4tgzKE2ZtG6ssCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"043e8f35669b933c35a4ab15300fdc50cb3d328667ee1f1d821d4d840dc22a76","last_reissued_at":"2026-07-05T06:27:57.389681Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:27:57.389681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2307.01503","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":"2026-07-05T06:27:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0uV9veUxYDpErsoExTO5OrzER8QLMlJpnQ0GKCE9l0u/B0K6sfp7f4jwXIiNnz5vlduMbwu/KOeYx+/rjMyQBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:12:54.464222Z"},"content_sha256":"4fd126d41bc0545d7ed93efdf1359b0c8583b4f20e2244f63b3030fcf5bab3b0","schema_version":"1.0","event_id":"sha256:4fd126d41bc0545d7ed93efdf1359b0c8583b4f20e2244f63b3030fcf5bab3b0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:AQ7I6NLGTOJTYNNEVMKTAD64KD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"On Evaluating and Mitigating Gender Biases in Multilingual Settings","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aniket Vashishtha, Kabir Ahuja, Sunayana Sitaram","submitted_at":"2023-07-04T06:23:04Z","abstract_excerpt":"While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English. In this work, we investigate some of the challenges with evaluating and mitigating biases in multilingual settings which stem from a lack of existing benchmarks and resources for bias evaluation beyond English especially for non-western context. In this paper, we first create a benchmark for evaluating gender biases in pre-trained masked language models by extending DisCo to different Indian languages using"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01503","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/2307.01503/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":"2026-07-05T06:27:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SRTx7owlgoa48mru7rWkiMwj3+S9rMxE1l2THWBb6LA4PW8NRrWal5wwOZkg8+lxkVQaEv7oV5KlWF5VDEVJCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T02:12:54.464794Z"},"content_sha256":"0253d8b64b61fb8ebb9211e33cdc6d323f17e42d84b5997d6d7a3880cf6a35a5","schema_version":"1.0","event_id":"sha256:0253d8b64b61fb8ebb9211e33cdc6d323f17e42d84b5997d6d7a3880cf6a35a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD/bundle.json","state_url":"https://pith.science/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD/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":"2026-08-05T02:12:54Z","links":{"resolver":"https://pith.science/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD","bundle":"https://pith.science/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD/bundle.json","state":"https://pith.science/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AQ7I6NLGTOJTYNNEVMKTAD64KD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:AQ7I6NLGTOJTYNNEVMKTAD64KD","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":"501efa12f881c670e03f7cbe39d973069bf760781d6fc6c23d558005c18c80a1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-04T06:23:04Z","title_canon_sha256":"6fad14874fb4737b7180602ba8fe592568dcf53f6aaeb1916410b7514d951267"},"schema_version":"1.0","source":{"id":"2307.01503","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2307.01503","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"arxiv_version","alias_value":"2307.01503v1","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.01503","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"pith_short_12","alias_value":"AQ7I6NLGTOJT","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"pith_short_16","alias_value":"AQ7I6NLGTOJTYNNE","created_at":"2026-07-05T06:27:57Z"},{"alias_kind":"pith_short_8","alias_value":"AQ7I6NLG","created_at":"2026-07-05T06:27:57Z"}],"graph_snapshots":[{"event_id":"sha256:0253d8b64b61fb8ebb9211e33cdc6d323f17e42d84b5997d6d7a3880cf6a35a5","target":"graph","created_at":"2026-07-05T06:27:57Z","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/2307.01503/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While understanding and removing gender biases in language models has been a long-standing problem in Natural Language Processing, prior research work has primarily been limited to English. In this work, we investigate some of the challenges with evaluating and mitigating biases in multilingual settings which stem from a lack of existing benchmarks and resources for bias evaluation beyond English especially for non-western context. In this paper, we first create a benchmark for evaluating gender biases in pre-trained masked language models by extending DisCo to different Indian languages using","authors_text":"Aniket Vashishtha, Kabir Ahuja, Sunayana Sitaram","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-04T06:23:04Z","title":"On Evaluating and Mitigating Gender Biases in Multilingual Settings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.01503","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":"sha256:4fd126d41bc0545d7ed93efdf1359b0c8583b4f20e2244f63b3030fcf5bab3b0","target":"record","created_at":"2026-07-05T06:27:57Z","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":"501efa12f881c670e03f7cbe39d973069bf760781d6fc6c23d558005c18c80a1","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-07-04T06:23:04Z","title_canon_sha256":"6fad14874fb4737b7180602ba8fe592568dcf53f6aaeb1916410b7514d951267"},"schema_version":"1.0","source":{"id":"2307.01503","kind":"arxiv","version":1}},"canonical_sha256":"043e8f35669b933c35a4ab15300fdc50cb3d328667ee1f1d821d4d840dc22a76","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"043e8f35669b933c35a4ab15300fdc50cb3d328667ee1f1d821d4d840dc22a76","first_computed_at":"2026-07-05T06:27:57.389681Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:27:57.389681Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"afnQecL1EJZZPQ73dWJL+9Tl4ncCMYuBFIj1R6GlFjPORG1fTbqfvZjOjtm7GMKAAY42XwV4tgzKE2ZtG6ssCQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:27:57.390075Z","signed_message":"canonical_sha256_bytes"},"source_id":"2307.01503","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fd126d41bc0545d7ed93efdf1359b0c8583b4f20e2244f63b3030fcf5bab3b0","sha256:0253d8b64b61fb8ebb9211e33cdc6d323f17e42d84b5997d6d7a3880cf6a35a5"],"state_sha256":"5bfc8672e7bd564db89e1dc9ed2e3a8f2d9547cb995002021dc8644cf01f468a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JX4inx3sVAkv8jIdyD9LkiXOOTJGbcnsKFFj5LCPb+8wwn5eiYlVWpHJenC7hpABP+U+Y6c5nLhGXKUBxJdZBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T02:12:54.468351Z","bundle_sha256":"fcd09051259b502a01ff310942680d7d318d66919ce8c1b386baf1e077ecec14"}}