{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:62NVHEXXBKPJQWRDKFLXED3ELQ","short_pith_number":"pith:62NVHEXX","canonical_record":{"source":{"id":"2401.08350","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-16T13:30:09Z","cross_cats_sorted":[],"title_canon_sha256":"8cf52824b8dfd083dface22897dcce4aeca371817fcdee63245e3898caf4c903","abstract_canon_sha256":"00e7c2be4e6442b05644862526b71e5b5c1bbc4ef743e49364ae56965d0998b4"},"schema_version":"1.0"},"canonical_sha256":"f69b5392f70a9e985a235157720f645c0754a0a6619b79903fe209b66decb753","source":{"kind":"arxiv","id":"2401.08350","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.08350","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"arxiv_version","alias_value":"2401.08350v3","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08350","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"pith_short_12","alias_value":"62NVHEXXBKPJ","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"pith_short_16","alias_value":"62NVHEXXBKPJQWRD","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"pith_short_8","alias_value":"62NVHEXX","created_at":"2026-07-05T09:48:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:62NVHEXXBKPJQWRDKFLXED3ELQ","target":"record","payload":{"canonical_record":{"source":{"id":"2401.08350","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-16T13:30:09Z","cross_cats_sorted":[],"title_canon_sha256":"8cf52824b8dfd083dface22897dcce4aeca371817fcdee63245e3898caf4c903","abstract_canon_sha256":"00e7c2be4e6442b05644862526b71e5b5c1bbc4ef743e49364ae56965d0998b4"},"schema_version":"1.0"},"canonical_sha256":"f69b5392f70a9e985a235157720f645c0754a0a6619b79903fe209b66decb753","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:48:30.549238Z","signature_b64":"WJJvCkGGoNUgzWdJvoe4LJg3+kWwUNgBtx+nHVd9jeSfPlpDfQ3k/eSSSVkQBBZitAgdhcjQtpNUuK/wTbbMDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f69b5392f70a9e985a235157720f645c0754a0a6619b79903fe209b66decb753","last_reissued_at":"2026-07-05T09:48:30.548753Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:48:30.548753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2401.08350","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:48:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CBF+7L+hSwCwY/D7Z0ZV4MsQgGTH73afqsuBWpKlatZFwuxqAd71hcrralmdVML+1sQTVQZnMbn3+ySPhbqFAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:49:32.486277Z"},"content_sha256":"c52175491d196492a9a5bca39f47e520bb22544e6a298d3e55a11cff7c6ba5ad","schema_version":"1.0","event_id":"sha256:c52175491d196492a9a5bca39f47e520bb22544e6a298d3e55a11cff7c6ba5ad"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:62NVHEXXBKPJQWRDKFLXED3ELQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Derek F. Wong, Dian Yu, Fanghua Ye, Jianhui Pang, Longyue Wang, Shuming Shi, Zhaopeng Tu","submitted_at":"2024-01-16T13:30:09Z","abstract_excerpt":"The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which have acted as benchmarks for progress in this field. This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search. Our empirical findings indicate that LLMs effectively lessen the reliance on parallel data for major langu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08350","kind":"arxiv","version":3},"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/2401.08350/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":"2026-07-05T09:48:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"12TzpkNmkssUH1EkqgzwH2+lg9511pihArpH5V8n6GfgR4W6tW7AbQzXqgut/jMXyjy3P7v12M3KZvERAghlAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T05:49:32.487083Z"},"content_sha256":"8f46bbcd503018f0b0a15427e62342f181826c527608471cb7e0a75035839f40","schema_version":"1.0","event_id":"sha256:8f46bbcd503018f0b0a15427e62342f181826c527608471cb7e0a75035839f40"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/62NVHEXXBKPJQWRDKFLXED3ELQ/bundle.json","state_url":"https://pith.science/pith/62NVHEXXBKPJQWRDKFLXED3ELQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/62NVHEXXBKPJQWRDKFLXED3ELQ/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":"2026-08-09T05:49:32Z","links":{"resolver":"https://pith.science/pith/62NVHEXXBKPJQWRDKFLXED3ELQ","bundle":"https://pith.science/pith/62NVHEXXBKPJQWRDKFLXED3ELQ/bundle.json","state":"https://pith.science/pith/62NVHEXXBKPJQWRDKFLXED3ELQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/62NVHEXXBKPJQWRDKFLXED3ELQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:62NVHEXXBKPJQWRDKFLXED3ELQ","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":"00e7c2be4e6442b05644862526b71e5b5c1bbc4ef743e49364ae56965d0998b4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-16T13:30:09Z","title_canon_sha256":"8cf52824b8dfd083dface22897dcce4aeca371817fcdee63245e3898caf4c903"},"schema_version":"1.0","source":{"id":"2401.08350","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.08350","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"arxiv_version","alias_value":"2401.08350v3","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.08350","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"pith_short_12","alias_value":"62NVHEXXBKPJ","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"pith_short_16","alias_value":"62NVHEXXBKPJQWRD","created_at":"2026-07-05T09:48:30Z"},{"alias_kind":"pith_short_8","alias_value":"62NVHEXX","created_at":"2026-07-05T09:48:30Z"}],"graph_snapshots":[{"event_id":"sha256:8f46bbcd503018f0b0a15427e62342f181826c527608471cb7e0a75035839f40","target":"graph","created_at":"2026-07-05T09:48:30Z","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/2401.08350/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The evolution of Neural Machine Translation (NMT) has been significantly influenced by six core challenges (Koehn and Knowles, 2017), which have acted as benchmarks for progress in this field. This study revisits these challenges, offering insights into their ongoing relevance in the context of advanced Large Language Models (LLMs): domain mismatch, amount of parallel data, rare word prediction, translation of long sentences, attention model as word alignment, and sub-optimal beam search. Our empirical findings indicate that LLMs effectively lessen the reliance on parallel data for major langu","authors_text":"Derek F. Wong, Dian Yu, Fanghua Ye, Jianhui Pang, Longyue Wang, Shuming Shi, Zhaopeng Tu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-16T13:30:09Z","title":"Salute the Classic: Revisiting Challenges of Machine Translation in the Age of Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.08350","kind":"arxiv","version":3},"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:c52175491d196492a9a5bca39f47e520bb22544e6a298d3e55a11cff7c6ba5ad","target":"record","created_at":"2026-07-05T09:48:30Z","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":"00e7c2be4e6442b05644862526b71e5b5c1bbc4ef743e49364ae56965d0998b4","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-01-16T13:30:09Z","title_canon_sha256":"8cf52824b8dfd083dface22897dcce4aeca371817fcdee63245e3898caf4c903"},"schema_version":"1.0","source":{"id":"2401.08350","kind":"arxiv","version":3}},"canonical_sha256":"f69b5392f70a9e985a235157720f645c0754a0a6619b79903fe209b66decb753","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f69b5392f70a9e985a235157720f645c0754a0a6619b79903fe209b66decb753","first_computed_at":"2026-07-05T09:48:30.548753Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:48:30.548753Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WJJvCkGGoNUgzWdJvoe4LJg3+kWwUNgBtx+nHVd9jeSfPlpDfQ3k/eSSSVkQBBZitAgdhcjQtpNUuK/wTbbMDw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:48:30.549238Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.08350","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c52175491d196492a9a5bca39f47e520bb22544e6a298d3e55a11cff7c6ba5ad","sha256:8f46bbcd503018f0b0a15427e62342f181826c527608471cb7e0a75035839f40"],"state_sha256":"530c9e05ae78874d78a1b6786a5065d580f816e6ed5057b6f540388437f55f84"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SsMJk2wDzldM2v1vnBkMq5vXg4tHd0jHLGq1nGLWrZPNBk7ftTtfeObhrRdGf9CVPqUEMrwrd7zZCQeqw/6yCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T05:49:32.493613Z","bundle_sha256":"9c470778a20abe9891b12fdf01a5e636e4961ee316d1eb91d973e3f680f29027"}}