{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ETZGAYE6YSJD4HHN2TN4YMRH6L","short_pith_number":"pith:ETZGAYE6","schema_version":"1.0","canonical_sha256":"24f260609ec4923e1cedd4dbcc3227f2d838c0dfd7f89efd36f4aec8c4203438","source":{"kind":"arxiv","id":"2310.20246","version":5},"attestation_state":"computed","paper":{"title":"Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dongmei Zhang, Jia Li, Ming Gong, Ning Wu, Nuo Chen, Zinan Zheng","submitted_at":"2023-10-31T08:09:20Z","abstract_excerpt":"Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue of training data scarcity in xMR tasks. Based on the collected dataset, we pro"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.20246","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-31T08:09:20Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0f714e3aaab27828483ae7a16bbc3f3a115861134543267beb739be7398ea23a","abstract_canon_sha256":"5a6b898aabfc2546fe2890989cb82991fcd09a23e58306c3392087602da736e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:21:01.365885Z","signature_b64":"pl1rNi+GQvcXON/kzzQPZ0tM1EgYq//rIJK6gBOWTjnIIOLF8XotahQrNRV8OS+L62fD0U56UvGI1ksxTXI2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24f260609ec4923e1cedd4dbcc3227f2d838c0dfd7f89efd36f4aec8c4203438","last_reissued_at":"2026-07-05T09:21:01.365205Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:21:01.365205Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Dongmei Zhang, Jia Li, Ming Gong, Ning Wu, Nuo Chen, Zinan Zheng","submitted_at":"2023-10-31T08:09:20Z","abstract_excerpt":"Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in preserving efficacy in a multilingual context. To bridge this gap, this paper pioneers exploring and training powerful Multilingual Math Reasoning (xMR) LLMs. Firstly, by utilizing translation, we construct the first multilingual math reasoning instruction dataset, MGSM8KInstruct, encompassing ten distinct languages, thus addressing the issue of training data scarcity in xMR tasks. Based on the collected dataset, we pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.20246","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/2310.20246/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.20246","created_at":"2026-07-05T09:21:01.365280+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.20246v5","created_at":"2026-07-05T09:21:01.365280+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.20246","created_at":"2026-07-05T09:21:01.365280+00:00"},{"alias_kind":"pith_short_12","alias_value":"ETZGAYE6YSJD","created_at":"2026-07-05T09:21:01.365280+00:00"},{"alias_kind":"pith_short_16","alias_value":"ETZGAYE6YSJD4HHN","created_at":"2026-07-05T09:21:01.365280+00:00"},{"alias_kind":"pith_short_8","alias_value":"ETZGAYE6","created_at":"2026-07-05T09:21:01.365280+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11470","citing_title":"The Periodic Table of LLM Reasoning: A Structured Survey of Reasoning Paradigms, Methods, and Failure Modes","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2606.08728","citing_title":"Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery","ref_index":239,"is_internal_anchor":false},{"citing_arxiv_id":"2504.16155","citing_title":"PRIMETIME : Limits of LLMs in Temporal Primitives","ref_index":58,"is_internal_anchor":false},{"citing_arxiv_id":"2601.13262","citing_title":"CURE-Med: Curriculum-Informed Reinforcement Learning for Multilingual Medical Reasoning","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07522","citing_title":"WeatherSyn: An Instruction Tuning MLLM For Weather Forecasting Report Generation","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L","json":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L.json","graph_json":"https://pith.science/api/pith-number/ETZGAYE6YSJD4HHN2TN4YMRH6L/graph.json","events_json":"https://pith.science/api/pith-number/ETZGAYE6YSJD4HHN2TN4YMRH6L/events.json","paper":"https://pith.science/paper/ETZGAYE6"},"agent_actions":{"view_html":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L","download_json":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L.json","view_paper":"https://pith.science/paper/ETZGAYE6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.20246&json=true","fetch_graph":"https://pith.science/api/pith-number/ETZGAYE6YSJD4HHN2TN4YMRH6L/graph.json","fetch_events":"https://pith.science/api/pith-number/ETZGAYE6YSJD4HHN2TN4YMRH6L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L/action/storage_attestation","attest_author":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L/action/author_attestation","sign_citation":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L/action/citation_signature","submit_replication":"https://pith.science/pith/ETZGAYE6YSJD4HHN2TN4YMRH6L/action/replication_record"}},"created_at":"2026-07-05T09:21:01.365280+00:00","updated_at":"2026-07-05T09:21:01.365280+00:00"}