{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XCJEQBYWY4P7PBMFQ6MSJ3GEA3","short_pith_number":"pith:XCJEQBYW","schema_version":"1.0","canonical_sha256":"b892480716c71ff78585879924ecc406f021dda0a55bfe186eaa42475d2cbf07","source":{"kind":"arxiv","id":"2303.13780","version":4},"attestation_state":"computed","paper":{"title":"Towards Making the Most of ChatGPT for Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dacheng Tao, Keqin Peng, Liang Ding, Li Shen, Min Zhang, Qihuang Zhong, Xuebo Liu, Yuanxin Ouyang","submitted_at":"2023-03-24T03:35:21Z","abstract_excerpt":"ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this paper, we aim to further mine ChatGPT's translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose an optimal temperature setting and two ("},"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":"2303.13780","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-03-24T03:35:21Z","cross_cats_sorted":[],"title_canon_sha256":"7af36a264a2435a6550a13c74fed90312a98d458c9c30d62790db028f21d85bd","abstract_canon_sha256":"65fe5af9b47284e216ba86a46944c5e01cf47b86e60e9d2dfc019465e0818134"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:02:49.104197Z","signature_b64":"3vKpkQ3VM8LlRfsnDR+VkUHBa4Jm3Wz+UJi0LMwMltf0BfRg3IGWO3jaH9ivQ6lLvFKZS0D0zxZiW0bWRQt0Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b892480716c71ff78585879924ecc406f021dda0a55bfe186eaa42475d2cbf07","last_reissued_at":"2026-07-05T07:02:49.103457Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:02:49.103457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Making the Most of ChatGPT for Machine Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dacheng Tao, Keqin Peng, Liang Ding, Li Shen, Min Zhang, Qihuang Zhong, Xuebo Liu, Yuanxin Ouyang","submitted_at":"2023-03-24T03:35:21Z","abstract_excerpt":"ChatGPT shows remarkable capabilities for machine translation (MT). Several prior studies have shown that it achieves comparable results to commercial systems for high-resource languages, but lags behind in complex tasks, e.g., low-resource and distant-language-pairs translation. However, they usually adopt simple prompts which can not fully elicit the capability of ChatGPT. In this paper, we aim to further mine ChatGPT's translation ability by revisiting several aspects: temperature, task information, and domain information, and correspondingly propose an optimal temperature setting and two ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.13780","kind":"arxiv","version":4},"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/2303.13780/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":"2303.13780","created_at":"2026-07-05T07:02:49.103519+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.13780v4","created_at":"2026-07-05T07:02:49.103519+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.13780","created_at":"2026-07-05T07:02:49.103519+00:00"},{"alias_kind":"pith_short_12","alias_value":"XCJEQBYWY4P7","created_at":"2026-07-05T07:02:49.103519+00:00"},{"alias_kind":"pith_short_16","alias_value":"XCJEQBYWY4P7PBMF","created_at":"2026-07-05T07:02:49.103519+00:00"},{"alias_kind":"pith_short_8","alias_value":"XCJEQBYW","created_at":"2026-07-05T07:02:49.103519+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.07020","citing_title":"MADE: Beyond Scoring via a Multilingual Agentic Diagnosing Engine for Fine-Grained Evaluation Insights","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2305.12138","citing_title":"Exploring Code Analysis: Zero-Shot Insights on Syntax and Semantics with LLMs","ref_index":67,"is_internal_anchor":false},{"citing_arxiv_id":"2409.00557","citing_title":"Learning to Ask: When LLM Agents Meet Unclear Instruction","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09111","citing_title":"PS-TTS: Phonetic Synchronization in Text-to-Speech for Achieving Natural Automated Dubbing","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3","json":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3.json","graph_json":"https://pith.science/api/pith-number/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/graph.json","events_json":"https://pith.science/api/pith-number/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/events.json","paper":"https://pith.science/paper/XCJEQBYW"},"agent_actions":{"view_html":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3","download_json":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3.json","view_paper":"https://pith.science/paper/XCJEQBYW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.13780&json=true","fetch_graph":"https://pith.science/api/pith-number/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/graph.json","fetch_events":"https://pith.science/api/pith-number/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/action/storage_attestation","attest_author":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/action/author_attestation","sign_citation":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/action/citation_signature","submit_replication":"https://pith.science/pith/XCJEQBYWY4P7PBMFQ6MSJ3GEA3/action/replication_record"}},"created_at":"2026-07-05T07:02:49.103519+00:00","updated_at":"2026-07-05T07:02:49.103519+00:00"}