{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:YZWJREFOL7B47PEA3FI4EXTAMD","short_pith_number":"pith:YZWJREFO","canonical_record":{"source":{"id":"2408.00397","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-01T09:07:32Z","cross_cats_sorted":[],"title_canon_sha256":"734208e5036b699bd03cc717d935e860fc2d288d0f8d7771adbfd72166524512","abstract_canon_sha256":"d06c5d5bcecf0e7bb53483b41170760b44998ab0722c46a529248aaee839edab"},"schema_version":"1.0"},"canonical_sha256":"c66c9890ae5fc3cfbc80d951c25e6060d2972aa8a49b1dcf6a10e90af23853d0","source":{"kind":"arxiv","id":"2408.00397","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.00397","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"arxiv_version","alias_value":"2408.00397v1","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00397","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"pith_short_12","alias_value":"YZWJREFOL7B4","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"pith_short_16","alias_value":"YZWJREFOL7B47PEA","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"pith_short_8","alias_value":"YZWJREFO","created_at":"2026-07-05T08:51:03Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:YZWJREFOL7B47PEA3FI4EXTAMD","target":"record","payload":{"canonical_record":{"source":{"id":"2408.00397","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-01T09:07:32Z","cross_cats_sorted":[],"title_canon_sha256":"734208e5036b699bd03cc717d935e860fc2d288d0f8d7771adbfd72166524512","abstract_canon_sha256":"d06c5d5bcecf0e7bb53483b41170760b44998ab0722c46a529248aaee839edab"},"schema_version":"1.0"},"canonical_sha256":"c66c9890ae5fc3cfbc80d951c25e6060d2972aa8a49b1dcf6a10e90af23853d0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:51:03.038306Z","signature_b64":"/BVR9aVNdg+e/x67OQAl+IcH7sCnhfMjLfJVCUPyOr/0USIfPxY4BGhonELQDXLJ/AdldYpJzbESSA56lkKkAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c66c9890ae5fc3cfbc80d951c25e6060d2972aa8a49b1dcf6a10e90af23853d0","last_reissued_at":"2026-07-05T08:51:03.037899Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:51:03.037899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2408.00397","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:51:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nlf3xV8ozHn2tWfxBicFPRQ2rfy9s3wdS76eByUZ7GeEUkKdurxdd/Ay5tE1G3KkXmUhwCXAksTC85ry6yi4Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T03:15:16.740908Z"},"content_sha256":"ab26c28e52fd1b46e51eb7e34fd97df46d5b4d294da39f482326adcb10074443","schema_version":"1.0","event_id":"sha256:ab26c28e52fd1b46e51eb7e34fd97df46d5b4d294da39f482326adcb10074443"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:YZWJREFOL7B47PEA3FI4EXTAMD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Armel Zebaze, Beno\\^it Sagot, Rachel Bawden","submitted_at":"2024-08-01T09:07:32Z","abstract_excerpt":"The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. In this paper, we focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. However no systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection over random selection. We provide a study covering multiple LLMs and multiple in-context example "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00397","kind":"arxiv","version":1},"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/2408.00397/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":"2026-07-05T08:51:03Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"eqU9udD0GYnc/96JaJ9dfSszxvkbOisrJVeWbCEHt9jjYv3tmXDOVrm3xO2ADY66jRUnW1PIuKiuOHggqh0aAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T03:15:16.741399Z"},"content_sha256":"e55cd4cc82b2f531d8f094c706eb50bd9f40e4a8e17273935605f6def3c796c3","schema_version":"1.0","event_id":"sha256:e55cd4cc82b2f531d8f094c706eb50bd9f40e4a8e17273935605f6def3c796c3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YZWJREFOL7B47PEA3FI4EXTAMD/bundle.json","state_url":"https://pith.science/pith/YZWJREFOL7B47PEA3FI4EXTAMD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YZWJREFOL7B47PEA3FI4EXTAMD/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":"2026-08-09T03:15:16Z","links":{"resolver":"https://pith.science/pith/YZWJREFOL7B47PEA3FI4EXTAMD","bundle":"https://pith.science/pith/YZWJREFOL7B47PEA3FI4EXTAMD/bundle.json","state":"https://pith.science/pith/YZWJREFOL7B47PEA3FI4EXTAMD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YZWJREFOL7B47PEA3FI4EXTAMD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:YZWJREFOL7B47PEA3FI4EXTAMD","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":"d06c5d5bcecf0e7bb53483b41170760b44998ab0722c46a529248aaee839edab","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-01T09:07:32Z","title_canon_sha256":"734208e5036b699bd03cc717d935e860fc2d288d0f8d7771adbfd72166524512"},"schema_version":"1.0","source":{"id":"2408.00397","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2408.00397","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"arxiv_version","alias_value":"2408.00397v1","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00397","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"pith_short_12","alias_value":"YZWJREFOL7B4","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"pith_short_16","alias_value":"YZWJREFOL7B47PEA","created_at":"2026-07-05T08:51:03Z"},{"alias_kind":"pith_short_8","alias_value":"YZWJREFO","created_at":"2026-07-05T08:51:03Z"}],"graph_snapshots":[{"event_id":"sha256:e55cd4cc82b2f531d8f094c706eb50bd9f40e4a8e17273935605f6def3c796c3","target":"graph","created_at":"2026-07-05T08:51:03Z","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/2408.00397/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The ability of generative large language models (LLMs) to perform in-context learning has given rise to a large body of research into how best to prompt models for various natural language processing tasks. In this paper, we focus on machine translation (MT), a task that has been shown to benefit from in-context translation examples. However no systematic studies have been published on how best to select examples, and mixed results have been reported on the usefulness of similarity-based selection over random selection. We provide a study covering multiple LLMs and multiple in-context example ","authors_text":"Armel Zebaze, Beno\\^it Sagot, Rachel Bawden","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-01T09:07:32Z","title":"In-Context Example Selection via Similarity Search Improves Low-Resource Machine Translation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00397","kind":"arxiv","version":1},"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:ab26c28e52fd1b46e51eb7e34fd97df46d5b4d294da39f482326adcb10074443","target":"record","created_at":"2026-07-05T08:51:03Z","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":"d06c5d5bcecf0e7bb53483b41170760b44998ab0722c46a529248aaee839edab","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-01T09:07:32Z","title_canon_sha256":"734208e5036b699bd03cc717d935e860fc2d288d0f8d7771adbfd72166524512"},"schema_version":"1.0","source":{"id":"2408.00397","kind":"arxiv","version":1}},"canonical_sha256":"c66c9890ae5fc3cfbc80d951c25e6060d2972aa8a49b1dcf6a10e90af23853d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c66c9890ae5fc3cfbc80d951c25e6060d2972aa8a49b1dcf6a10e90af23853d0","first_computed_at":"2026-07-05T08:51:03.037899Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:51:03.037899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/BVR9aVNdg+e/x67OQAl+IcH7sCnhfMjLfJVCUPyOr/0USIfPxY4BGhonELQDXLJ/AdldYpJzbESSA56lkKkAw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:51:03.038306Z","signed_message":"canonical_sha256_bytes"},"source_id":"2408.00397","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab26c28e52fd1b46e51eb7e34fd97df46d5b4d294da39f482326adcb10074443","sha256:e55cd4cc82b2f531d8f094c706eb50bd9f40e4a8e17273935605f6def3c796c3"],"state_sha256":"93ebea0fe57430b66aada3043a12456b252a75893ae5be673bd0c7dcfd8a7ea3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZkzJiqmtx4AKwdEjNfaCJfL0PxjOc8unl5ymBoVSTN8moNxMY5onvnjCrj4yxoqH45TS2Ad2luUUu7FKSRa3CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T03:15:16.745933Z","bundle_sha256":"4d798be17dc519fb2410ddc6b953d34fff6c576ab0fcdf98d9416998decd1d76"}}