{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:QUMZCWWHJ7APQC67ZFZB7DPFS7","short_pith_number":"pith:QUMZCWWH","canonical_record":{"source":{"id":"2405.20089","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T14:25:56Z","cross_cats_sorted":[],"title_canon_sha256":"542b49fb3c5ef59d5fcd10ce5d788470ac9ebddc3208ea3a99d662736481aab9","abstract_canon_sha256":"910140b659006d1538424afc769eef77b9f8e1014557372f4d52f240c798f560"},"schema_version":"1.0"},"canonical_sha256":"8519915ac74fc0f80bdfc9721f8de597d012c594242029c309ffd111f92628d7","source":{"kind":"arxiv","id":"2405.20089","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.20089","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"arxiv_version","alias_value":"2405.20089v2","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20089","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"pith_short_12","alias_value":"QUMZCWWHJ7AP","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"pith_short_16","alias_value":"QUMZCWWHJ7APQC67","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"pith_short_8","alias_value":"QUMZCWWH","created_at":"2026-07-05T08:52:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:QUMZCWWHJ7APQC67ZFZB7DPFS7","target":"record","payload":{"canonical_record":{"source":{"id":"2405.20089","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T14:25:56Z","cross_cats_sorted":[],"title_canon_sha256":"542b49fb3c5ef59d5fcd10ce5d788470ac9ebddc3208ea3a99d662736481aab9","abstract_canon_sha256":"910140b659006d1538424afc769eef77b9f8e1014557372f4d52f240c798f560"},"schema_version":"1.0"},"canonical_sha256":"8519915ac74fc0f80bdfc9721f8de597d012c594242029c309ffd111f92628d7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:52:33.524283Z","signature_b64":"3OKEsJpZQgRsnxGf86EZ7fA6lG/eV6cdq3FhL7hanhGinPdECkryhQ2KZFjOGsokIgtzDK6ck6+3/rBtQVNbDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8519915ac74fc0f80bdfc9721f8de597d012c594242029c309ffd111f92628d7","last_reissued_at":"2026-07-05T08:52:33.523860Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:52:33.523860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.20089","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:52:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1TmUF/abiRRu8pG0xd3BG7QVMvR6IsXzVJoFsTqLKv+oK9FEuVTBJyf3hIlraC0onQwkpx1nmcpoUqTqNFu7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:06:45.224535Z"},"content_sha256":"61d9d244ab15efaa64564acf2249eeaf70e125a9308d2ff97af008da04677c9d","schema_version":"1.0","event_id":"sha256:61d9d244ab15efaa64564acf2249eeaf70e125a9308d2ff97af008da04677c9d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:QUMZCWWHJ7APQC67ZFZB7DPFS7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Bill Byrne, Christof Monz, David Stap, Eva Hasler, Ke Tran","submitted_at":"2024-05-30T14:25:56Z","abstract_excerpt":"Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality. However, it is unclear what is the impact of fine-tuning on desirable LLM behaviors that are not present in neural machine translation models, such as steerability, inherent document-level translation abilities, and the ability to produce less literal translations. We perform an extensive translation evaluation on the LLaMA and Falcon family of models with model size ranging from 7 billion up to 65 billion parameters. Our results show that while fine-tuning improves the gener"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20089","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/2405.20089/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":"2026-07-05T08:52:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yPveq6GOPygsAMN5cNk1HGTnHvAeQ46d9yIY99V96BALIf2DfdA7U5/r/vZB13Q0CfQbwpyUanHIowvJRzceBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:06:45.225045Z"},"content_sha256":"a255ba6297504d95a2ddfce9cd7704b2b3fc281f87ae4f4b160f409dbe8c1674","schema_version":"1.0","event_id":"sha256:a255ba6297504d95a2ddfce9cd7704b2b3fc281f87ae4f4b160f409dbe8c1674"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7/bundle.json","state_url":"https://pith.science/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7/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":"2026-08-08T13:06:45Z","links":{"resolver":"https://pith.science/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7","bundle":"https://pith.science/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7/bundle.json","state":"https://pith.science/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QUMZCWWHJ7APQC67ZFZB7DPFS7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QUMZCWWHJ7APQC67ZFZB7DPFS7","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":"910140b659006d1538424afc769eef77b9f8e1014557372f4d52f240c798f560","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T14:25:56Z","title_canon_sha256":"542b49fb3c5ef59d5fcd10ce5d788470ac9ebddc3208ea3a99d662736481aab9"},"schema_version":"1.0","source":{"id":"2405.20089","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.20089","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"arxiv_version","alias_value":"2405.20089v2","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20089","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"pith_short_12","alias_value":"QUMZCWWHJ7AP","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"pith_short_16","alias_value":"QUMZCWWHJ7APQC67","created_at":"2026-07-05T08:52:33Z"},{"alias_kind":"pith_short_8","alias_value":"QUMZCWWH","created_at":"2026-07-05T08:52:33Z"}],"graph_snapshots":[{"event_id":"sha256:a255ba6297504d95a2ddfce9cd7704b2b3fc281f87ae4f4b160f409dbe8c1674","target":"graph","created_at":"2026-07-05T08:52:33Z","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/2405.20089/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fine-tuning large language models (LLMs) for machine translation has shown improvements in overall translation quality. However, it is unclear what is the impact of fine-tuning on desirable LLM behaviors that are not present in neural machine translation models, such as steerability, inherent document-level translation abilities, and the ability to produce less literal translations. We perform an extensive translation evaluation on the LLaMA and Falcon family of models with model size ranging from 7 billion up to 65 billion parameters. Our results show that while fine-tuning improves the gener","authors_text":"Bill Byrne, Christof Monz, David Stap, Eva Hasler, Ke Tran","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T14:25:56Z","title":"The Fine-Tuning Paradox: Boosting Translation Quality Without Sacrificing LLM Abilities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20089","kind":"arxiv","version":2},"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":"sha256:61d9d244ab15efaa64564acf2249eeaf70e125a9308d2ff97af008da04677c9d","target":"record","created_at":"2026-07-05T08:52:33Z","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":"910140b659006d1538424afc769eef77b9f8e1014557372f4d52f240c798f560","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-05-30T14:25:56Z","title_canon_sha256":"542b49fb3c5ef59d5fcd10ce5d788470ac9ebddc3208ea3a99d662736481aab9"},"schema_version":"1.0","source":{"id":"2405.20089","kind":"arxiv","version":2}},"canonical_sha256":"8519915ac74fc0f80bdfc9721f8de597d012c594242029c309ffd111f92628d7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8519915ac74fc0f80bdfc9721f8de597d012c594242029c309ffd111f92628d7","first_computed_at":"2026-07-05T08:52:33.523860Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:52:33.523860Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3OKEsJpZQgRsnxGf86EZ7fA6lG/eV6cdq3FhL7hanhGinPdECkryhQ2KZFjOGsokIgtzDK6ck6+3/rBtQVNbDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:52:33.524283Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.20089","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:61d9d244ab15efaa64564acf2249eeaf70e125a9308d2ff97af008da04677c9d","sha256:a255ba6297504d95a2ddfce9cd7704b2b3fc281f87ae4f4b160f409dbe8c1674"],"state_sha256":"be88490328c23df9e79d35e3c71ea4d7d887c248a99421131ee1a601df564852"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NQVw60k5jnGZDfsbVrY2EFYfH4o7RO6VgKEh/uIRdPHdh2mCOT9nYDxVTYyWqIgcwBWbJ7HK8QGNHLGJvMmwAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:06:45.229595Z","bundle_sha256":"186dc6f5eb501712e78b7f5d67d1e1f0f4b92b86ff021fb6c6a89810ab3408b6"}}