{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CE3YVNTPTLJEHEMRLYRERPMWSN","short_pith_number":"pith:CE3YVNTP","canonical_record":{"source":{"id":"2407.06432","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-08T22:22:15Z","cross_cats_sorted":[],"title_canon_sha256":"4ab69c29097a55e84f45a90180838d848d05a07a160555383d092db1e25ff39e","abstract_canon_sha256":"0e8293bf19bf5490ce1bfdf5f0a412cf830c4d41303bfd214a8a6b2363d7cfbf"},"schema_version":"1.0"},"canonical_sha256":"11378ab66f9ad24391915e2248bd9693748853d91b6e1e8d8c8182d371aeff66","source":{"kind":"arxiv","id":"2407.06432","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.06432","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"arxiv_version","alias_value":"2407.06432v1","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.06432","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"pith_short_12","alias_value":"CE3YVNTPTLJE","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"pith_short_16","alias_value":"CE3YVNTPTLJEHEMR","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"pith_short_8","alias_value":"CE3YVNTP","created_at":"2026-07-05T08:41:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CE3YVNTPTLJEHEMRLYRERPMWSN","target":"record","payload":{"canonical_record":{"source":{"id":"2407.06432","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-08T22:22:15Z","cross_cats_sorted":[],"title_canon_sha256":"4ab69c29097a55e84f45a90180838d848d05a07a160555383d092db1e25ff39e","abstract_canon_sha256":"0e8293bf19bf5490ce1bfdf5f0a412cf830c4d41303bfd214a8a6b2363d7cfbf"},"schema_version":"1.0"},"canonical_sha256":"11378ab66f9ad24391915e2248bd9693748853d91b6e1e8d8c8182d371aeff66","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:48.612902Z","signature_b64":"//ui1sFG+TIZKr1yzBqUfiO534P93ioPHjz3LBLIuIKyajSXlsem7WdcgCgOl7Q+uwRHojNqq0oocVvdh3j1BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"11378ab66f9ad24391915e2248bd9693748853d91b6e1e8d8c8182d371aeff66","last_reissued_at":"2026-07-05T08:41:48.612504Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:48.612504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.06432","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":"2026-07-05T08:41:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ALFIQWcvNwdUt38Jf8Wb5OCjcNtALgLP+OyDIRf5DSwknY4VtjmF8ZtiC4MwdcP3z6FH9QVmuszk35luxL/XBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T16:06:09.352072Z"},"content_sha256":"a55a85501a4c130824fa4a87af10454650b2e73705d66f2dba6704da6c88eb02","schema_version":"1.0","event_id":"sha256:a55a85501a4c130824fa4a87af10454650b2e73705d66f2dba6704da6c88eb02"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CE3YVNTPTLJEHEMRLYRERPMWSN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jayanta Sadhu, Maneesha Rani Saha, Rifat Shahriyar","submitted_at":"2024-07-08T22:22:15Z","abstract_excerpt":"The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there's a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.06432","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/2407.06432/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":"2026-07-05T08:41:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v4vgq0x/xhb71WiR3br+GdW2Q0bcdgSNCtRx2xYWOrjxyOJ88b40xiywN2qbZftwvW37FdC99czNi37c840jAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T16:06:09.352657Z"},"content_sha256":"c91eb6a705ebee61e8f0d18184d100679226120aacbfccecd31a582d6688a0e1","schema_version":"1.0","event_id":"sha256:c91eb6a705ebee61e8f0d18184d100679226120aacbfccecd31a582d6688a0e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CE3YVNTPTLJEHEMRLYRERPMWSN/bundle.json","state_url":"https://pith.science/pith/CE3YVNTPTLJEHEMRLYRERPMWSN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CE3YVNTPTLJEHEMRLYRERPMWSN/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":"2026-08-09T16:06:09Z","links":{"resolver":"https://pith.science/pith/CE3YVNTPTLJEHEMRLYRERPMWSN","bundle":"https://pith.science/pith/CE3YVNTPTLJEHEMRLYRERPMWSN/bundle.json","state":"https://pith.science/pith/CE3YVNTPTLJEHEMRLYRERPMWSN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CE3YVNTPTLJEHEMRLYRERPMWSN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CE3YVNTPTLJEHEMRLYRERPMWSN","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":"0e8293bf19bf5490ce1bfdf5f0a412cf830c4d41303bfd214a8a6b2363d7cfbf","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-08T22:22:15Z","title_canon_sha256":"4ab69c29097a55e84f45a90180838d848d05a07a160555383d092db1e25ff39e"},"schema_version":"1.0","source":{"id":"2407.06432","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.06432","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"arxiv_version","alias_value":"2407.06432v1","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.06432","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"pith_short_12","alias_value":"CE3YVNTPTLJE","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"pith_short_16","alias_value":"CE3YVNTPTLJEHEMR","created_at":"2026-07-05T08:41:48Z"},{"alias_kind":"pith_short_8","alias_value":"CE3YVNTP","created_at":"2026-07-05T08:41:48Z"}],"graph_snapshots":[{"event_id":"sha256:c91eb6a705ebee61e8f0d18184d100679226120aacbfccecd31a582d6688a0e1","target":"graph","created_at":"2026-07-05T08:41:48Z","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/2407.06432/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The influence of Large Language Models (LLMs) is rapidly growing, automating more jobs over time. Assessing the fairness of LLMs is crucial due to their expanding impact. Studies reveal the reflection of societal norms and biases in LLMs, which creates a risk of propagating societal stereotypes in downstream tasks. Many studies on bias in LLMs focus on gender bias in various NLP applications. However, there's a gap in research on bias in emotional attributes, despite the close societal link between emotion and gender. This gap is even larger for low-resource languages like Bangla. Historically","authors_text":"Jayanta Sadhu, Maneesha Rani Saha, Rifat Shahriyar","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-08T22:22:15Z","title":"An Empirical Study of Gendered Stereotypes in Emotional Attributes for Bangla in Multilingual Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.06432","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:a55a85501a4c130824fa4a87af10454650b2e73705d66f2dba6704da6c88eb02","target":"record","created_at":"2026-07-05T08:41:48Z","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":"0e8293bf19bf5490ce1bfdf5f0a412cf830c4d41303bfd214a8a6b2363d7cfbf","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-07-08T22:22:15Z","title_canon_sha256":"4ab69c29097a55e84f45a90180838d848d05a07a160555383d092db1e25ff39e"},"schema_version":"1.0","source":{"id":"2407.06432","kind":"arxiv","version":1}},"canonical_sha256":"11378ab66f9ad24391915e2248bd9693748853d91b6e1e8d8c8182d371aeff66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"11378ab66f9ad24391915e2248bd9693748853d91b6e1e8d8c8182d371aeff66","first_computed_at":"2026-07-05T08:41:48.612504Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:41:48.612504Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"//ui1sFG+TIZKr1yzBqUfiO534P93ioPHjz3LBLIuIKyajSXlsem7WdcgCgOl7Q+uwRHojNqq0oocVvdh3j1BA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:41:48.612902Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.06432","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a55a85501a4c130824fa4a87af10454650b2e73705d66f2dba6704da6c88eb02","sha256:c91eb6a705ebee61e8f0d18184d100679226120aacbfccecd31a582d6688a0e1"],"state_sha256":"7e3751cf767d9f7e3ac69a2f0a05e77953c7304a8c822691410496447c2e31e4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UbloMprnvmgZiAaOJAoMmWo7dntfVyL2WzbKauAHZGHnOd1mAMiIWHBn4PEXmWhk5HvBxXLSe3iy1sg9mDgaAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T16:06:09.356703Z","bundle_sha256":"e6330e1397e654273a163d6bbd47e40b91a308610f5df9b57c31c5afc9d8c431"}}