{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:2AI3B6MIPWANR5YODQUKCDJEAX","short_pith_number":"pith:2AI3B6MI","canonical_record":{"source":{"id":"2303.00807","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-03-01T20:21:23Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"19921f468d9e905172fa449d012868350dec8f870d5dd1d646501e225f5e748b","abstract_canon_sha256":"700f34b52d5b99637d688f095dee2039f06852c0bb1f51ab68c4bf202ae2138d"},"schema_version":"1.0"},"canonical_sha256":"d011b0f9887d80d8f70e1c28a10d2405f943c06f89265ef9e45c798e103f2b1e","source":{"kind":"arxiv","id":"2303.00807","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.00807","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"arxiv_version","alias_value":"2303.00807v3","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.00807","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"pith_short_12","alias_value":"2AI3B6MIPWAN","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"pith_short_16","alias_value":"2AI3B6MIPWANR5YO","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"pith_short_8","alias_value":"2AI3B6MI","created_at":"2026-07-05T07:00:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:2AI3B6MIPWANR5YODQUKCDJEAX","target":"record","payload":{"canonical_record":{"source":{"id":"2303.00807","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-03-01T20:21:23Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"19921f468d9e905172fa449d012868350dec8f870d5dd1d646501e225f5e748b","abstract_canon_sha256":"700f34b52d5b99637d688f095dee2039f06852c0bb1f51ab68c4bf202ae2138d"},"schema_version":"1.0"},"canonical_sha256":"d011b0f9887d80d8f70e1c28a10d2405f943c06f89265ef9e45c798e103f2b1e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:00:21.646509Z","signature_b64":"LGEz7+pEulWA5WWtr4YDHigZ+0FTyY7GJ6achOMLqHhsDOozdvOJULpQbKksJfJItL6XRrDSHN7LGui3QG+KDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d011b0f9887d80d8f70e1c28a10d2405f943c06f89265ef9e45c798e103f2b1e","last_reissued_at":"2026-07-05T07:00:21.646039Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:00:21.646039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2303.00807","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":"2026-07-05T07:00:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O4kukPhgvk3XH7unNAAP41b7ov0ZZqu1f8OxNVRK7Iho78kQh/mUNniS+yUPBfrdnpG6zsU1bXdOD9zk/rhcCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:30:44.011149Z"},"content_sha256":"f6b5c0a4f59e6e8e98b8d81c99b851ea05c0c9f26b1680b85f4a3e598d5ed071","schema_version":"1.0","event_id":"sha256:f6b5c0a4f59e6e8e98b8d81c99b851ea05c0c9f26b1680b85f4a3e598d5ed071"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:2AI3B6MIPWANR5YODQUKCDJEAX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.IR","authors_text":"Avirup Sil, Christopher Potts, Jon Saad-Falcon, Keshav Santhanam, Martin Franz, Md Arafat Sultan, Omar Khattab, Radu Florian, Salim Roukos","submitted_at":"2023-03-01T20:21:23Z","abstract_excerpt":"Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we develop and motivate a method for using large language models (LLMs) to generate large numbers of synthetic queries cheaply. The method begins by generating a small number of synthetic queries using an expensive LLM. After that, a much less expensive one is used to create large numbers of synthetic queries, which are used to fine-tune a family of reran"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.00807","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/2303.00807/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":"2026-07-05T07:00:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8bVENhW/ZzpYC2OPAhG/VWgJemSColkc/rHch5WnrLJKdRuGfuvC+8XTqkTcstGj0aK5VIOwFdmCV1T4ElXlDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:30:44.011540Z"},"content_sha256":"d276145967c6115fc054a23f97e7c6b43145e290a744c58409e4f092f857aafb","schema_version":"1.0","event_id":"sha256:d276145967c6115fc054a23f97e7c6b43145e290a744c58409e4f092f857aafb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2AI3B6MIPWANR5YODQUKCDJEAX/bundle.json","state_url":"https://pith.science/pith/2AI3B6MIPWANR5YODQUKCDJEAX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2AI3B6MIPWANR5YODQUKCDJEAX/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":"2026-07-06T23:30:44Z","links":{"resolver":"https://pith.science/pith/2AI3B6MIPWANR5YODQUKCDJEAX","bundle":"https://pith.science/pith/2AI3B6MIPWANR5YODQUKCDJEAX/bundle.json","state":"https://pith.science/pith/2AI3B6MIPWANR5YODQUKCDJEAX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2AI3B6MIPWANR5YODQUKCDJEAX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:2AI3B6MIPWANR5YODQUKCDJEAX","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":"700f34b52d5b99637d688f095dee2039f06852c0bb1f51ab68c4bf202ae2138d","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-03-01T20:21:23Z","title_canon_sha256":"19921f468d9e905172fa449d012868350dec8f870d5dd1d646501e225f5e748b"},"schema_version":"1.0","source":{"id":"2303.00807","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2303.00807","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"arxiv_version","alias_value":"2303.00807v3","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.00807","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"pith_short_12","alias_value":"2AI3B6MIPWAN","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"pith_short_16","alias_value":"2AI3B6MIPWANR5YO","created_at":"2026-07-05T07:00:21Z"},{"alias_kind":"pith_short_8","alias_value":"2AI3B6MI","created_at":"2026-07-05T07:00:21Z"}],"graph_snapshots":[{"event_id":"sha256:d276145967c6115fc054a23f97e7c6b43145e290a744c58409e4f092f857aafb","target":"graph","created_at":"2026-07-05T07:00:21Z","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/2303.00807/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Many information retrieval tasks require large labeled datasets for fine-tuning. However, such datasets are often unavailable, and their utility for real-world applications can diminish quickly due to domain shifts. To address this challenge, we develop and motivate a method for using large language models (LLMs) to generate large numbers of synthetic queries cheaply. The method begins by generating a small number of synthetic queries using an expensive LLM. After that, a much less expensive one is used to create large numbers of synthetic queries, which are used to fine-tune a family of reran","authors_text":"Avirup Sil, Christopher Potts, Jon Saad-Falcon, Keshav Santhanam, Martin Franz, Md Arafat Sultan, Omar Khattab, Radu Florian, Salim Roukos","cross_cats":["cs.CL"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-03-01T20:21:23Z","title":"UDAPDR: Unsupervised Domain Adaptation via LLM Prompting and Distillation of Rerankers"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.00807","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:f6b5c0a4f59e6e8e98b8d81c99b851ea05c0c9f26b1680b85f4a3e598d5ed071","target":"record","created_at":"2026-07-05T07:00:21Z","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":"700f34b52d5b99637d688f095dee2039f06852c0bb1f51ab68c4bf202ae2138d","cross_cats_sorted":["cs.CL"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2023-03-01T20:21:23Z","title_canon_sha256":"19921f468d9e905172fa449d012868350dec8f870d5dd1d646501e225f5e748b"},"schema_version":"1.0","source":{"id":"2303.00807","kind":"arxiv","version":3}},"canonical_sha256":"d011b0f9887d80d8f70e1c28a10d2405f943c06f89265ef9e45c798e103f2b1e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d011b0f9887d80d8f70e1c28a10d2405f943c06f89265ef9e45c798e103f2b1e","first_computed_at":"2026-07-05T07:00:21.646039Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:00:21.646039Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LGEz7+pEulWA5WWtr4YDHigZ+0FTyY7GJ6achOMLqHhsDOozdvOJULpQbKksJfJItL6XRrDSHN7LGui3QG+KDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:00:21.646509Z","signed_message":"canonical_sha256_bytes"},"source_id":"2303.00807","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6b5c0a4f59e6e8e98b8d81c99b851ea05c0c9f26b1680b85f4a3e598d5ed071","sha256:d276145967c6115fc054a23f97e7c6b43145e290a744c58409e4f092f857aafb"],"state_sha256":"f9ef765125876208ee670941d6a931f6b9d8c39603767c40d14f0b1e6c55a448"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"avx3gnq5x0bge51yZwRGJtTYDF0bVTI6vzkG2ALwldhKdUHSPSCm1VB7eVAp42vf+onMO+NLItPakXvdb+WbDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:30:44.013614Z","bundle_sha256":"f580f407d8a5e1d2a3e6dbcf9206396dcff02fd77545485950f0a8d54d2e1e1d"}}