{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UP4WBG47HLUDC7WLARBZCG6EPD","short_pith_number":"pith:UP4WBG47","schema_version":"1.0","canonical_sha256":"a3f9609b9f3ae8317ecb0443911bc478edd4933c18f4373785ed3527e76a733e","source":{"kind":"arxiv","id":"2507.09509","version":2},"attestation_state":"computed","paper":{"title":"How Important is `Perfect' English for Machine Translation Prompts?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Gianluca Vico, Katharina H\\\"ammerl, Niyati Bafna, Patr\\'icia Schmidtov\\'a, Seth Aycock, Vil\\'em Zouhar, Wiktor Kamzela","submitted_at":"2025-07-13T06:33:12Z","abstract_excerpt":"Large language models (LLMs) have achieved top results in recent machine translation evaluations, but they are also known to be sensitive to errors and perturbations in their prompts. We systematically evaluate how both humanly plausible and synthetic errors in user prompts affect LLMs' performance on two related tasks: Machine translation and machine translation evaluation. We provide both a quantitative analysis and qualitative insights into how the models respond to increasing noise in the user prompt.\n  The prompt quality strongly affects the translation performance: With many errors, even"},"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":"2507.09509","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-13T06:33:12Z","cross_cats_sorted":[],"title_canon_sha256":"c1d00fbaa80501894b5c512bbe792082013aedd26a91baef0ec9edd6b41e0a1a","abstract_canon_sha256":"2cbd774b124fa9fb846b5e01239d877a2a3f4b160ab0cae2dcf421a692646a39"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:02:03.585891Z","signature_b64":"ir78YWwDSWc5ZveXwLMGMfx0ok7nyi4bOD0BpwdcHbwcO+ip1JIEn0Bh6e8EkUkwge35PC1idsugs/rbGPSlBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3f9609b9f3ae8317ecb0443911bc478edd4933c18f4373785ed3527e76a733e","last_reissued_at":"2026-07-05T12:02:03.585393Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:02:03.585393Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Important is `Perfect' English for Machine Translation Prompts?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Gianluca Vico, Katharina H\\\"ammerl, Niyati Bafna, Patr\\'icia Schmidtov\\'a, Seth Aycock, Vil\\'em Zouhar, Wiktor Kamzela","submitted_at":"2025-07-13T06:33:12Z","abstract_excerpt":"Large language models (LLMs) have achieved top results in recent machine translation evaluations, but they are also known to be sensitive to errors and perturbations in their prompts. We systematically evaluate how both humanly plausible and synthetic errors in user prompts affect LLMs' performance on two related tasks: Machine translation and machine translation evaluation. We provide both a quantitative analysis and qualitative insights into how the models respond to increasing noise in the user prompt.\n  The prompt quality strongly affects the translation performance: With many errors, even"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.09509","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/2507.09509/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":"2507.09509","created_at":"2026-07-05T12:02:03.585445+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.09509v2","created_at":"2026-07-05T12:02:03.585445+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.09509","created_at":"2026-07-05T12:02:03.585445+00:00"},{"alias_kind":"pith_short_12","alias_value":"UP4WBG47HLUD","created_at":"2026-07-05T12:02:03.585445+00:00"},{"alias_kind":"pith_short_16","alias_value":"UP4WBG47HLUDC7WL","created_at":"2026-07-05T12:02:03.585445+00:00"},{"alias_kind":"pith_short_8","alias_value":"UP4WBG47","created_at":"2026-07-05T12:02:03.585445+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD","json":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD.json","graph_json":"https://pith.science/api/pith-number/UP4WBG47HLUDC7WLARBZCG6EPD/graph.json","events_json":"https://pith.science/api/pith-number/UP4WBG47HLUDC7WLARBZCG6EPD/events.json","paper":"https://pith.science/paper/UP4WBG47"},"agent_actions":{"view_html":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD","download_json":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD.json","view_paper":"https://pith.science/paper/UP4WBG47","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.09509&json=true","fetch_graph":"https://pith.science/api/pith-number/UP4WBG47HLUDC7WLARBZCG6EPD/graph.json","fetch_events":"https://pith.science/api/pith-number/UP4WBG47HLUDC7WLARBZCG6EPD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD/action/storage_attestation","attest_author":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD/action/author_attestation","sign_citation":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD/action/citation_signature","submit_replication":"https://pith.science/pith/UP4WBG47HLUDC7WLARBZCG6EPD/action/replication_record"}},"created_at":"2026-07-05T12:02:03.585445+00:00","updated_at":"2026-07-05T12:02:03.585445+00:00"}