{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:U6T5G3W4TAJZV5Q7AKCVR6KCVW","short_pith_number":"pith:U6T5G3W4","canonical_record":{"source":{"id":"2311.07978","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:10:14Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c77d06f92b4adba0f1a5b0ac35c6aa0746f8b4a18eba7c46219825a9e64fd5e4","abstract_canon_sha256":"8953b1242eff71dae2d88832390d1ccdb96be89c1478256b19543b20c1881876"},"schema_version":"1.0"},"canonical_sha256":"a7a7d36edc98139af61f028558f942ad9f8cef72ffda7dcf7d563c0a5c05c8d6","source":{"kind":"arxiv","id":"2311.07978","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.07978","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"arxiv_version","alias_value":"2311.07978v5","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07978","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"pith_short_12","alias_value":"U6T5G3W4TAJZ","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"pith_short_16","alias_value":"U6T5G3W4TAJZV5Q7","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"pith_short_8","alias_value":"U6T5G3W4","created_at":"2026-07-05T11:17:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:U6T5G3W4TAJZV5Q7AKCVR6KCVW","target":"record","payload":{"canonical_record":{"source":{"id":"2311.07978","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:10:14Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c77d06f92b4adba0f1a5b0ac35c6aa0746f8b4a18eba7c46219825a9e64fd5e4","abstract_canon_sha256":"8953b1242eff71dae2d88832390d1ccdb96be89c1478256b19543b20c1881876"},"schema_version":"1.0"},"canonical_sha256":"a7a7d36edc98139af61f028558f942ad9f8cef72ffda7dcf7d563c0a5c05c8d6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:31.592885Z","signature_b64":"Nvoh+fvwqPT4AuNDMy0FPxUfL8o2OZzBi4A1vR88DTK2ilS+nmF45MCIp1rgjYt9/fO0NEEaghriFEDuToZwCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7a7d36edc98139af61f028558f942ad9f8cef72ffda7dcf7d563c0a5c05c8d6","last_reissued_at":"2026-07-05T11:17:31.592334Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:31.592334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.07978","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aFeeuZGc2mixmOtoSfi+M83M4RIGt2qiZ3rX+HuM95SGKc71le7KKkvqwmVeRXHu7UKTykwkHgR+Xijuv2KvCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T10:53:47.947062Z"},"content_sha256":"1283ba3f95bd6c9d8cd659fd660f802ce122e3cc8dd7c27beb1e52244de83263","schema_version":"1.0","event_id":"sha256:1283ba3f95bd6c9d8cd659fd660f802ce122e3cc8dd7c27beb1e52244de83263"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:U6T5G3W4TAJZV5Q7AKCVR6KCVW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AfroBench: How Good are Large Language Models on African Languages?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Akintunde Oladipo, David Ifeoluwa Adelani, Jessica Ojo, Jimmy Lin, Kelechi Ogueji, Odunayo Ogundepo, Pontus Stenetorp","submitted_at":"2023-11-14T08:10:14Z","abstract_excerpt":"Large-scale multilingual evaluations, such as MEGA, often include only a handful of African languages due to the scarcity of high-quality evaluation data and the limited discoverability of existing African datasets. This lack of representation hinders comprehensive LLM evaluation across a diverse range of languages and tasks. To address these challenges, we introduce AfroBench -- a multi-task benchmark for evaluating the performance of LLMs across 64 African languages, 15 tasks and 22 datasets. AfroBench consists of nine natural language understanding datasets, six text generation datasets, si"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07978","kind":"arxiv","version":5},"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/2311.07978/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":"2026-07-05T11:17:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xy8ZehDzRFmlG0JvdYi3ZyxOiT8S6hR6cxN1pyEOemv7yHKvb7XOK7VjUL7ySjrKJk6m9f2cTJNl5jhR0eYuCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T10:53:47.947403Z"},"content_sha256":"2c7e332a1538e2b58944210713f9e9040841c0b7d0712178f197343145e8a5e9","schema_version":"1.0","event_id":"sha256:2c7e332a1538e2b58944210713f9e9040841c0b7d0712178f197343145e8a5e9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW/bundle.json","state_url":"https://pith.science/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW/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":"2026-08-22T10:53:47Z","links":{"resolver":"https://pith.science/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW","bundle":"https://pith.science/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW/bundle.json","state":"https://pith.science/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U6T5G3W4TAJZV5Q7AKCVR6KCVW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:U6T5G3W4TAJZV5Q7AKCVR6KCVW","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":"8953b1242eff71dae2d88832390d1ccdb96be89c1478256b19543b20c1881876","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:10:14Z","title_canon_sha256":"c77d06f92b4adba0f1a5b0ac35c6aa0746f8b4a18eba7c46219825a9e64fd5e4"},"schema_version":"1.0","source":{"id":"2311.07978","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.07978","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"arxiv_version","alias_value":"2311.07978v5","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.07978","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"pith_short_12","alias_value":"U6T5G3W4TAJZ","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"pith_short_16","alias_value":"U6T5G3W4TAJZV5Q7","created_at":"2026-07-05T11:17:31Z"},{"alias_kind":"pith_short_8","alias_value":"U6T5G3W4","created_at":"2026-07-05T11:17:31Z"}],"graph_snapshots":[{"event_id":"sha256:2c7e332a1538e2b58944210713f9e9040841c0b7d0712178f197343145e8a5e9","target":"graph","created_at":"2026-07-05T11:17:31Z","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/2311.07978/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large-scale multilingual evaluations, such as MEGA, often include only a handful of African languages due to the scarcity of high-quality evaluation data and the limited discoverability of existing African datasets. This lack of representation hinders comprehensive LLM evaluation across a diverse range of languages and tasks. To address these challenges, we introduce AfroBench -- a multi-task benchmark for evaluating the performance of LLMs across 64 African languages, 15 tasks and 22 datasets. AfroBench consists of nine natural language understanding datasets, six text generation datasets, si","authors_text":"Akintunde Oladipo, David Ifeoluwa Adelani, Jessica Ojo, Jimmy Lin, Kelechi Ogueji, Odunayo Ogundepo, Pontus Stenetorp","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:10:14Z","title":"AfroBench: How Good are Large Language Models on African Languages?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.07978","kind":"arxiv","version":5},"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":"sha256:1283ba3f95bd6c9d8cd659fd660f802ce122e3cc8dd7c27beb1e52244de83263","target":"record","created_at":"2026-07-05T11:17:31Z","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":"8953b1242eff71dae2d88832390d1ccdb96be89c1478256b19543b20c1881876","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-14T08:10:14Z","title_canon_sha256":"c77d06f92b4adba0f1a5b0ac35c6aa0746f8b4a18eba7c46219825a9e64fd5e4"},"schema_version":"1.0","source":{"id":"2311.07978","kind":"arxiv","version":5}},"canonical_sha256":"a7a7d36edc98139af61f028558f942ad9f8cef72ffda7dcf7d563c0a5c05c8d6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7a7d36edc98139af61f028558f942ad9f8cef72ffda7dcf7d563c0a5c05c8d6","first_computed_at":"2026-07-05T11:17:31.592334Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:31.592334Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Nvoh+fvwqPT4AuNDMy0FPxUfL8o2OZzBi4A1vR88DTK2ilS+nmF45MCIp1rgjYt9/fO0NEEaghriFEDuToZwCA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:31.592885Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.07978","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1283ba3f95bd6c9d8cd659fd660f802ce122e3cc8dd7c27beb1e52244de83263","sha256:2c7e332a1538e2b58944210713f9e9040841c0b7d0712178f197343145e8a5e9"],"state_sha256":"c48730683696e35cdd19a7d029cc036d026032c4390e466ce52ad520f643f2ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BoikdDJzdA8lZK+8wRhJ+hG0Gwo7nqZWMIfgUuXmQJmE1J219qkBCcIVj82dMF3hBaF7w2OR0qL+51dNSLOLDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T10:53:47.950441Z","bundle_sha256":"c7c48e6c1d6ef29ab53a7322cb73a37a454d8c482723e6579e0c5396d92036e4"}}