{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:Z4QJ67IAR4R4MFRBNRNJ6UBV3N","short_pith_number":"pith:Z4QJ67IA","canonical_record":{"source":{"id":"2205.09726","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-19T17:36:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a538391bfd78769f93d42b773a0e485d810a9b490667fa1f8eb03457a78cf8f3","abstract_canon_sha256":"fc9e4ff4b40d9811cd173af1f0433059090349670403ca1eeb819c88b8651438"},"schema_version":"1.0"},"canonical_sha256":"cf209f7d008f23c616216c5a9f5035db6197d62bbca58bd1cfafe262bf2d8355","source":{"kind":"arxiv","id":"2205.09726","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09726","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09726v3","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09726","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"pith_short_12","alias_value":"Z4QJ67IAR4R4","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"pith_short_16","alias_value":"Z4QJ67IAR4R4MFRB","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"pith_short_8","alias_value":"Z4QJ67IA","created_at":"2026-07-05T05:16:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:Z4QJ67IAR4R4MFRBNRNJ6UBV3N","target":"record","payload":{"canonical_record":{"source":{"id":"2205.09726","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-19T17:36:46Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a538391bfd78769f93d42b773a0e485d810a9b490667fa1f8eb03457a78cf8f3","abstract_canon_sha256":"fc9e4ff4b40d9811cd173af1f0433059090349670403ca1eeb819c88b8651438"},"schema_version":"1.0"},"canonical_sha256":"cf209f7d008f23c616216c5a9f5035db6197d62bbca58bd1cfafe262bf2d8355","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:16:00.417265Z","signature_b64":"b1uD8HKV6kGGW86SI6QwD9pxih/CYaGBJq1QbOQqKH+pAoaNMCH52izylDX9gHzCXhLJrLbqXg6aKNDojS+UCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cf209f7d008f23c616216c5a9f5035db6197d62bbca58bd1cfafe262bf2d8355","last_reissued_at":"2026-07-05T05:16:00.416826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:16:00.416826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2205.09726","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":"2026-07-05T05:16:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tT7Zz2GTEqAJjbsfDgO2ApO8Ab8JS/jSgAD4YbNaQovWmkgsfZ8dRa8fcpAF3p/iowXcURKac8gNafGmZUCsCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:45:01.398462Z"},"content_sha256":"96568dcf0b89834e74847bfc20356e84765ba73bc7882bb0441f34651d5402f8","schema_version":"1.0","event_id":"sha256:96568dcf0b89834e74847bfc20356e84765ba73bc7882bb0441f34651d5402f8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:Z4QJ67IAR4R4MFRBNRNJ6UBV3N","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RankGen: Improving Text Generation with Large Ranking Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"John Wieting, Kalpesh Krishna, Mohit Iyyer, Yapei Chang","submitted_at":"2022-05-19T17:36:46Z","abstract_excerpt":"Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To address these issues we present RankGen, a 1.2B parameter encoder model for English that scores model generations given a prefix. RankGen can be flexibly incorporated as a scoring function in beam search and used to decode from any pretrained language model. We train RankGen using large-scale contrastive learning to map a prefix close to the ground-truth sequen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09726","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/2205.09726/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":"2026-07-05T05:16:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3upQUEodi3dgUItS6i6OBqw0knD0U9a76x8afaW9CNarWssLdicvdaJznggnThAWG6XX9nq0Pl3Av/JmbpKICQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T21:45:01.398945Z"},"content_sha256":"2d7d30de3b105857424dcd0754f6f0e1ceb755bb2015794fc5abd6ccb465754a","schema_version":"1.0","event_id":"sha256:2d7d30de3b105857424dcd0754f6f0e1ceb755bb2015794fc5abd6ccb465754a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N/bundle.json","state_url":"https://pith.science/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N/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":"2026-08-08T21:45:01Z","links":{"resolver":"https://pith.science/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N","bundle":"https://pith.science/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N/bundle.json","state":"https://pith.science/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z4QJ67IAR4R4MFRBNRNJ6UBV3N/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:Z4QJ67IAR4R4MFRBNRNJ6UBV3N","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":"fc9e4ff4b40d9811cd173af1f0433059090349670403ca1eeb819c88b8651438","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-19T17:36:46Z","title_canon_sha256":"a538391bfd78769f93d42b773a0e485d810a9b490667fa1f8eb03457a78cf8f3"},"schema_version":"1.0","source":{"id":"2205.09726","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2205.09726","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"arxiv_version","alias_value":"2205.09726v3","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.09726","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"pith_short_12","alias_value":"Z4QJ67IAR4R4","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"pith_short_16","alias_value":"Z4QJ67IAR4R4MFRB","created_at":"2026-07-05T05:16:00Z"},{"alias_kind":"pith_short_8","alias_value":"Z4QJ67IA","created_at":"2026-07-05T05:16:00Z"}],"graph_snapshots":[{"event_id":"sha256:2d7d30de3b105857424dcd0754f6f0e1ceb755bb2015794fc5abd6ccb465754a","target":"graph","created_at":"2026-07-05T05:16:00Z","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/2205.09726/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Given an input sequence (or prefix), modern language models often assign high probabilities to output sequences that are repetitive, incoherent, or irrelevant to the prefix; as such, model-generated text also contains such artifacts. To address these issues we present RankGen, a 1.2B parameter encoder model for English that scores model generations given a prefix. RankGen can be flexibly incorporated as a scoring function in beam search and used to decode from any pretrained language model. We train RankGen using large-scale contrastive learning to map a prefix close to the ground-truth sequen","authors_text":"John Wieting, Kalpesh Krishna, Mohit Iyyer, Yapei Chang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-19T17:36:46Z","title":"RankGen: Improving Text Generation with Large Ranking Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.09726","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":"sha256:96568dcf0b89834e74847bfc20356e84765ba73bc7882bb0441f34651d5402f8","target":"record","created_at":"2026-07-05T05:16:00Z","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":"fc9e4ff4b40d9811cd173af1f0433059090349670403ca1eeb819c88b8651438","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-19T17:36:46Z","title_canon_sha256":"a538391bfd78769f93d42b773a0e485d810a9b490667fa1f8eb03457a78cf8f3"},"schema_version":"1.0","source":{"id":"2205.09726","kind":"arxiv","version":3}},"canonical_sha256":"cf209f7d008f23c616216c5a9f5035db6197d62bbca58bd1cfafe262bf2d8355","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cf209f7d008f23c616216c5a9f5035db6197d62bbca58bd1cfafe262bf2d8355","first_computed_at":"2026-07-05T05:16:00.416826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:16:00.416826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b1uD8HKV6kGGW86SI6QwD9pxih/CYaGBJq1QbOQqKH+pAoaNMCH52izylDX9gHzCXhLJrLbqXg6aKNDojS+UCg==","signature_status":"signed_v1","signed_at":"2026-07-05T05:16:00.417265Z","signed_message":"canonical_sha256_bytes"},"source_id":"2205.09726","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:96568dcf0b89834e74847bfc20356e84765ba73bc7882bb0441f34651d5402f8","sha256:2d7d30de3b105857424dcd0754f6f0e1ceb755bb2015794fc5abd6ccb465754a"],"state_sha256":"638f568f75d6ead78f9cc6f08b05a105b88a9530f032e09f4756cd9b7add13cf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"04vXPdCEqDlLYsgNK3b10bcCBLxXJPtLdGhb+WegNfCdwd9tAxFXchj0VBRC9PG6H1dLcxL9jQr1q1HT9aKqDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T21:45:01.403643Z","bundle_sha256":"54ac5ecafe6cd6dd86ca2e8ebd09eb68a2460a25ddd46f9aa1abdac502a401dc"}}