{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:DI7JWBO3XFMVD7UAS2F7QFRBPZ","short_pith_number":"pith:DI7JWBO3","canonical_record":{"source":{"id":"2302.04813","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-09T18:02:34Z","cross_cats_sorted":[],"title_canon_sha256":"4b92ae6f33c4e9f4271c9678ab093ac466a4669e053cb1224ad73e980455a0c8","abstract_canon_sha256":"eb7627078082a0160929fc36d931931767f117cfb7588c44d9348047cde9cd8f"},"schema_version":"1.0"},"canonical_sha256":"1a3e9b05dbb95951fe80968bf816217e4e66bd3404a68c0cffd76b35b6cca6c6","source":{"kind":"arxiv","id":"2302.04813","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.04813","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"2302.04813v3","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04813","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"DI7JWBO3XFMV","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"DI7JWBO3XFMVD7UA","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"DI7JWBO3","created_at":"2026-07-05T07:01:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:DI7JWBO3XFMVD7UAS2F7QFRBPZ","target":"record","payload":{"canonical_record":{"source":{"id":"2302.04813","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-09T18:02:34Z","cross_cats_sorted":[],"title_canon_sha256":"4b92ae6f33c4e9f4271c9678ab093ac466a4669e053cb1224ad73e980455a0c8","abstract_canon_sha256":"eb7627078082a0160929fc36d931931767f117cfb7588c44d9348047cde9cd8f"},"schema_version":"1.0"},"canonical_sha256":"1a3e9b05dbb95951fe80968bf816217e4e66bd3404a68c0cffd76b35b6cca6c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:58.873827Z","signature_b64":"jdDWmwcZJJXUaq8rOGn6I5ts/1tg8wzUQGNpQcYE3++pV/NFdkKek3JWmI7tbJmrboxkaai3LZyUE+zHtjJKCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1a3e9b05dbb95951fe80968bf816217e4e66bd3404a68c0cffd76b35b6cca6c6","last_reissued_at":"2026-07-05T07:01:58.873321Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:58.873321Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2302.04813","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":"2026-07-05T07:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MJoU1r6WMmQiD9EOH3YCOsSPPPK38qyDSJZGnv2JvliseeWzbSoYwAlF/h5bzaNlfXnDu1LDRpWnqcF1rK8/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:29:40.728446Z"},"content_sha256":"254371f2264f7ee62623cf05c7a5a1730827cf21df8f818a154041892bf2b67f","schema_version":"1.0","event_id":"sha256:254371f2264f7ee62623cf05c7a5a1730827cf21df8f818a154041892bf2b67f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:DI7JWBO3XFMVD7UAS2F7QFRBPZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Greg Durrett, Xi Ye","submitted_at":"2023-02-09T18:02:34Z","abstract_excerpt":"Recent work has shown how to prompt large language models with explanations to obtain strong performance on textual reasoning tasks, i.e., the chain-of-thought paradigm. However, subtly different explanations can yield widely varying downstream task accuracy. Explanations that have not been \"tuned\" for a task, such as off-the-shelf explanations written by nonexperts, may lead to mediocre performance. This paper tackles the problem of how to optimize explanation-infused prompts in a blackbox fashion. We first generate sets of candidate explanations for each example in the prompt using a leave-o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04813","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/2302.04813/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":"2026-07-05T07:01:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+oRFp72uYb8YVxuPkGOJNuib2EQyDPB7+bmu0Z5rGpL745eAxIL0RKwc13Yjd1QJy/VzJddUrLM85EwgQhDwBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-07T18:29:40.728953Z"},"content_sha256":"7f9cc846e5c9aa371b8433ca9073557a83fffb8af9edf5fb29e89510d7bfd26d","schema_version":"1.0","event_id":"sha256:7f9cc846e5c9aa371b8433ca9073557a83fffb8af9edf5fb29e89510d7bfd26d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ/bundle.json","state_url":"https://pith.science/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ/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":"2026-08-07T18:29:40Z","links":{"resolver":"https://pith.science/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ","bundle":"https://pith.science/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ/bundle.json","state":"https://pith.science/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DI7JWBO3XFMVD7UAS2F7QFRBPZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DI7JWBO3XFMVD7UAS2F7QFRBPZ","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":"eb7627078082a0160929fc36d931931767f117cfb7588c44d9348047cde9cd8f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-09T18:02:34Z","title_canon_sha256":"4b92ae6f33c4e9f4271c9678ab093ac466a4669e053cb1224ad73e980455a0c8"},"schema_version":"1.0","source":{"id":"2302.04813","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2302.04813","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"arxiv_version","alias_value":"2302.04813v3","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.04813","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"pith_short_12","alias_value":"DI7JWBO3XFMV","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"pith_short_16","alias_value":"DI7JWBO3XFMVD7UA","created_at":"2026-07-05T07:01:58Z"},{"alias_kind":"pith_short_8","alias_value":"DI7JWBO3","created_at":"2026-07-05T07:01:58Z"}],"graph_snapshots":[{"event_id":"sha256:7f9cc846e5c9aa371b8433ca9073557a83fffb8af9edf5fb29e89510d7bfd26d","target":"graph","created_at":"2026-07-05T07:01:58Z","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/2302.04813/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent work has shown how to prompt large language models with explanations to obtain strong performance on textual reasoning tasks, i.e., the chain-of-thought paradigm. However, subtly different explanations can yield widely varying downstream task accuracy. Explanations that have not been \"tuned\" for a task, such as off-the-shelf explanations written by nonexperts, may lead to mediocre performance. This paper tackles the problem of how to optimize explanation-infused prompts in a blackbox fashion. We first generate sets of candidate explanations for each example in the prompt using a leave-o","authors_text":"Greg Durrett, Xi Ye","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-09T18:02:34Z","title":"Explanation Selection Using Unlabeled Data for Chain-of-Thought Prompting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.04813","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":"sha256:254371f2264f7ee62623cf05c7a5a1730827cf21df8f818a154041892bf2b67f","target":"record","created_at":"2026-07-05T07:01:58Z","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":"eb7627078082a0160929fc36d931931767f117cfb7588c44d9348047cde9cd8f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-02-09T18:02:34Z","title_canon_sha256":"4b92ae6f33c4e9f4271c9678ab093ac466a4669e053cb1224ad73e980455a0c8"},"schema_version":"1.0","source":{"id":"2302.04813","kind":"arxiv","version":3}},"canonical_sha256":"1a3e9b05dbb95951fe80968bf816217e4e66bd3404a68c0cffd76b35b6cca6c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1a3e9b05dbb95951fe80968bf816217e4e66bd3404a68c0cffd76b35b6cca6c6","first_computed_at":"2026-07-05T07:01:58.873321Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:58.873321Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jdDWmwcZJJXUaq8rOGn6I5ts/1tg8wzUQGNpQcYE3++pV/NFdkKek3JWmI7tbJmrboxkaai3LZyUE+zHtjJKCg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:58.873827Z","signed_message":"canonical_sha256_bytes"},"source_id":"2302.04813","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:254371f2264f7ee62623cf05c7a5a1730827cf21df8f818a154041892bf2b67f","sha256:7f9cc846e5c9aa371b8433ca9073557a83fffb8af9edf5fb29e89510d7bfd26d"],"state_sha256":"d66d0421b8ff3cafa60937f234df1db36148e4659f62e882a58a503a7eebe4c0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"W8dlaGhMw2++5kQVhyjXYFq6Nb0h1ZTgnBNMBJdYhWmy+ljFO23RPi1vzhXSychhl0VWombe+YAGC3w5fcsvCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-07T18:29:40.733021Z","bundle_sha256":"119fcd58dc06880d8acfa2f90de29a00931adf8a94dd2d72cc1fca986400a728"}}