{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:I2H2OZAP2FWMOWJNZMPT4XDUF4","short_pith_number":"pith:I2H2OZAP","schema_version":"1.0","canonical_sha256":"468fa7640fd16cc7592dcb1f3e5c742f17275b56f33f27951115c7a37272cdd7","source":{"kind":"arxiv","id":"2305.07004","version":2},"attestation_state":"computed","paper":{"title":"Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dongdong Zhang, Furu Wei, Haoyang Huang, Tianyi Tang, Ting Song, Wayne Xin Zhao, Yan Xia","submitted_at":"2023-05-11T17:44:17Z","abstract_excerpt":"Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both"},"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":"2305.07004","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-05-11T17:44:17Z","cross_cats_sorted":[],"title_canon_sha256":"a1f13359a9d3562d4e49a7ec89204bb75a424a1b1acedd6003a847c8671afc99","abstract_canon_sha256":"e9be45af68f7675f3d0d118e5f2b96b63a84a4808ddeba78fb9bfc806241660d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:03:24.270586Z","signature_b64":"9GIrXQMTnVzyTnhUOfBBfI+jqYyO0EXl8Y/+rpRHIlBq9b2h84Pqf0mdaKOovgVkaIvGzOL1e7kH1BiN/mVUDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"468fa7640fd16cc7592dcb1f3e5c742f17275b56f33f27951115c7a37272cdd7","last_reissued_at":"2026-07-05T07:03:24.270107Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:03:24.270107Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dongdong Zhang, Furu Wei, Haoyang Huang, Tianyi Tang, Ting Song, Wayne Xin Zhao, Yan Xia","submitted_at":"2023-05-11T17:44:17Z","abstract_excerpt":"Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. In this work, we introduce a simple yet effective method, called cross-lingual-thought prompting (XLT), to systematically improve the multilingual capability of LLMs. Specifically, XLT is a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. We conduct comprehensive evaluations on 7 typical benchmarks related to reasoning, understanding, and generation tasks, covering both"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.07004","kind":"arxiv","version":2},"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/2305.07004/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":"2305.07004","created_at":"2026-07-05T07:03:24.270159+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.07004v2","created_at":"2026-07-05T07:03:24.270159+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.07004","created_at":"2026-07-05T07:03:24.270159+00:00"},{"alias_kind":"pith_short_12","alias_value":"I2H2OZAP2FWM","created_at":"2026-07-05T07:03:24.270159+00:00"},{"alias_kind":"pith_short_16","alias_value":"I2H2OZAP2FWMOWJN","created_at":"2026-07-05T07:03:24.270159+00:00"},{"alias_kind":"pith_short_8","alias_value":"I2H2OZAP","created_at":"2026-07-05T07:03:24.270159+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2505.14990","citing_title":"Language Specific Knowledge: Do Models Know Better in X than in English?","ref_index":10,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4","json":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4.json","graph_json":"https://pith.science/api/pith-number/I2H2OZAP2FWMOWJNZMPT4XDUF4/graph.json","events_json":"https://pith.science/api/pith-number/I2H2OZAP2FWMOWJNZMPT4XDUF4/events.json","paper":"https://pith.science/paper/I2H2OZAP"},"agent_actions":{"view_html":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4","download_json":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4.json","view_paper":"https://pith.science/paper/I2H2OZAP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.07004&json=true","fetch_graph":"https://pith.science/api/pith-number/I2H2OZAP2FWMOWJNZMPT4XDUF4/graph.json","fetch_events":"https://pith.science/api/pith-number/I2H2OZAP2FWMOWJNZMPT4XDUF4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4/action/storage_attestation","attest_author":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4/action/author_attestation","sign_citation":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4/action/citation_signature","submit_replication":"https://pith.science/pith/I2H2OZAP2FWMOWJNZMPT4XDUF4/action/replication_record"}},"created_at":"2026-07-05T07:03:24.270159+00:00","updated_at":"2026-07-05T07:03:24.270159+00:00"}