{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SM5IB5H5H4BD7FL3NBJJ6OS6TE","short_pith_number":"pith:SM5IB5H5","canonical_record":{"source":{"id":"2501.01679","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-03T07:47:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2082d61e5d141b95d75812cb8b8ca130f0b577c22217504e6814465ac0953002","abstract_canon_sha256":"9908d81a0d290e0cb6755ea1b84bee847d98d74186d9e8229c5703a769cf7176"},"schema_version":"1.0"},"canonical_sha256":"933a80f4fd3f023f957b68529f3a5e993de2d8e1ba38b94875b01c555bbc8be9","source":{"kind":"arxiv","id":"2501.01679","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.01679","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"arxiv_version","alias_value":"2501.01679v1","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01679","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"pith_short_12","alias_value":"SM5IB5H5H4BD","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"pith_short_16","alias_value":"SM5IB5H5H4BD7FL3","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"pith_short_8","alias_value":"SM5IB5H5","created_at":"2026-07-05T09:56:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SM5IB5H5H4BD7FL3NBJJ6OS6TE","target":"record","payload":{"canonical_record":{"source":{"id":"2501.01679","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-03T07:47:59Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2082d61e5d141b95d75812cb8b8ca130f0b577c22217504e6814465ac0953002","abstract_canon_sha256":"9908d81a0d290e0cb6755ea1b84bee847d98d74186d9e8229c5703a769cf7176"},"schema_version":"1.0"},"canonical_sha256":"933a80f4fd3f023f957b68529f3a5e993de2d8e1ba38b94875b01c555bbc8be9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:56:28.726114Z","signature_b64":"Xnq59CeXVpnyiWFU6jEfCGuO/KTeWHvNv2kvj08AMDnsVFNMYEWIgcVaPw22tjtcsunjOfsXDoZJT/CBa1SyDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"933a80f4fd3f023f957b68529f3a5e993de2d8e1ba38b94875b01c555bbc8be9","last_reissued_at":"2026-07-05T09:56:28.725616Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:56:28.725616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2501.01679","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":"2026-07-05T09:56:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JXved4qugs9+d7IoCAGrn2m2ElL5pkP/L+QRb1Y6qmRhe2zcf2gPFsUbe/aYDBO5z6rmgg3Fhe/XwAabKGaTCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T13:30:26.799889Z"},"content_sha256":"7412d6eb920bcd4c8a4d9a97d6240b2b3315f520a1f937e12f7f26c1b122e074","schema_version":"1.0","event_id":"sha256:7412d6eb920bcd4c8a4d9a97d6240b2b3315f520a1f937e12f7f26c1b122e074"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SM5IB5H5H4BD7FL3NBJJ6OS6TE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hao Tan, Jinghui Qin, Lei Tang, Wenxuan Ye, Zhijing Yang","submitted_at":"2025-01-03T07:47:59Z","abstract_excerpt":"Recently, Large language models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that LLMs are prompt-sensitive and it is sub-optimal to apply the fixed prompt to any input for downstream machine translation tasks. To address this issue, we propose an adaptive few-shot prompting (AFSP) framework to automatically select suitable translation demonstrations for various source input sentences to further elicit the translation capability of an LLM for better machine translation. First, we build a translat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01679","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/2501.01679/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":"2026-07-05T09:56:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wzpeehrXGYM9f3sYxAgLQXixGVy3PBTKu6Bt9lgIWvkKCX3B2uFFIfWoLskQ+ZoNV3yVq6KIe23rbosDnByxCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T13:30:26.800268Z"},"content_sha256":"9f9ee556f865e60ce4f4fbb28b917043bde2ccf42216e404e02570158baae627","schema_version":"1.0","event_id":"sha256:9f9ee556f865e60ce4f4fbb28b917043bde2ccf42216e404e02570158baae627"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE/bundle.json","state_url":"https://pith.science/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE/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":"2026-08-13T13:30:26Z","links":{"resolver":"https://pith.science/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE","bundle":"https://pith.science/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE/bundle.json","state":"https://pith.science/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SM5IB5H5H4BD7FL3NBJJ6OS6TE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SM5IB5H5H4BD7FL3NBJJ6OS6TE","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":"9908d81a0d290e0cb6755ea1b84bee847d98d74186d9e8229c5703a769cf7176","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-03T07:47:59Z","title_canon_sha256":"2082d61e5d141b95d75812cb8b8ca130f0b577c22217504e6814465ac0953002"},"schema_version":"1.0","source":{"id":"2501.01679","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.01679","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"arxiv_version","alias_value":"2501.01679v1","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.01679","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"pith_short_12","alias_value":"SM5IB5H5H4BD","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"pith_short_16","alias_value":"SM5IB5H5H4BD7FL3","created_at":"2026-07-05T09:56:28Z"},{"alias_kind":"pith_short_8","alias_value":"SM5IB5H5","created_at":"2026-07-05T09:56:28Z"}],"graph_snapshots":[{"event_id":"sha256:9f9ee556f865e60ce4f4fbb28b917043bde2ccf42216e404e02570158baae627","target":"graph","created_at":"2026-07-05T09:56:28Z","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/2501.01679/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recently, Large language models (LLMs) with in-context learning have demonstrated remarkable potential in handling neural machine translation. However, existing evidence shows that LLMs are prompt-sensitive and it is sub-optimal to apply the fixed prompt to any input for downstream machine translation tasks. To address this issue, we propose an adaptive few-shot prompting (AFSP) framework to automatically select suitable translation demonstrations for various source input sentences to further elicit the translation capability of an LLM for better machine translation. First, we build a translat","authors_text":"Hao Tan, Jinghui Qin, Lei Tang, Wenxuan Ye, Zhijing Yang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-03T07:47:59Z","title":"Adaptive Few-shot Prompting for Machine Translation with Pre-trained Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.01679","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":"sha256:7412d6eb920bcd4c8a4d9a97d6240b2b3315f520a1f937e12f7f26c1b122e074","target":"record","created_at":"2026-07-05T09:56:28Z","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":"9908d81a0d290e0cb6755ea1b84bee847d98d74186d9e8229c5703a769cf7176","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-01-03T07:47:59Z","title_canon_sha256":"2082d61e5d141b95d75812cb8b8ca130f0b577c22217504e6814465ac0953002"},"schema_version":"1.0","source":{"id":"2501.01679","kind":"arxiv","version":1}},"canonical_sha256":"933a80f4fd3f023f957b68529f3a5e993de2d8e1ba38b94875b01c555bbc8be9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"933a80f4fd3f023f957b68529f3a5e993de2d8e1ba38b94875b01c555bbc8be9","first_computed_at":"2026-07-05T09:56:28.725616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:56:28.725616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xnq59CeXVpnyiWFU6jEfCGuO/KTeWHvNv2kvj08AMDnsVFNMYEWIgcVaPw22tjtcsunjOfsXDoZJT/CBa1SyDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:56:28.726114Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.01679","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7412d6eb920bcd4c8a4d9a97d6240b2b3315f520a1f937e12f7f26c1b122e074","sha256:9f9ee556f865e60ce4f4fbb28b917043bde2ccf42216e404e02570158baae627"],"state_sha256":"8afeaed2b16131b5f0b384adc73503b6aac3284b22dbc0e2b535bfce71ea74a6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P/9rQzNnwjxQjtNLMkUpMv0b69vv26GdtoRPPFEtSlLukRuIDFXyOdsZ6S3cyGCc3U7SLfEYeDqLEAB6FiYzAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T13:30:26.803112Z","bundle_sha256":"ac4e783c4de8b9bda1ccdad4d818ee665791835425cec132077ce9eb42f1de2d"}}