{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:RH36Y2F37HPTAWT36CDBTROFHI","short_pith_number":"pith:RH36Y2F3","canonical_record":{"source":{"id":"2311.16720","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-28T12:04:19Z","cross_cats_sorted":[],"title_canon_sha256":"50e1251c0c357c1d5438fce8a97416506b57981cca367b00752b9280a2eb3eef","abstract_canon_sha256":"bacb66b6e3defbc8fa9ed4fee53dec4d7f1e84af9f3becad0aa69821394cd921"},"schema_version":"1.0"},"canonical_sha256":"89f7ec68bbf9df305a7bf08619c5c53a2e320a32f7c89470d9ed1b104b97ffeb","source":{"kind":"arxiv","id":"2311.16720","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16720","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16720v3","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16720","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"pith_short_12","alias_value":"RH36Y2F37HPT","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"pith_short_16","alias_value":"RH36Y2F37HPTAWT3","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"pith_short_8","alias_value":"RH36Y2F3","created_at":"2026-07-05T08:25:51Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:RH36Y2F37HPTAWT36CDBTROFHI","target":"record","payload":{"canonical_record":{"source":{"id":"2311.16720","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-28T12:04:19Z","cross_cats_sorted":[],"title_canon_sha256":"50e1251c0c357c1d5438fce8a97416506b57981cca367b00752b9280a2eb3eef","abstract_canon_sha256":"bacb66b6e3defbc8fa9ed4fee53dec4d7f1e84af9f3becad0aa69821394cd921"},"schema_version":"1.0"},"canonical_sha256":"89f7ec68bbf9df305a7bf08619c5c53a2e320a32f7c89470d9ed1b104b97ffeb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:51.171645Z","signature_b64":"1/mIa1VLRlTP3bc7PblQ5HpkVns3HYC6pdme3EH+zfRbUJuWbX43wplfo91ikTYAUxEK7pNUy4F6ZSrdImxhDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89f7ec68bbf9df305a7bf08619c5c53a2e320a32f7c89470d9ed1b104b97ffeb","last_reissued_at":"2026-07-05T08:25:51.171169Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:51.171169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2311.16720","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":"2026-07-05T08:25:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1FhbawD6xG6BRp6eMVLoUVtxNps+wGS0d7K6+ibEcgrAmvQUcpxnQ0z/FRID+LjNFQEuqLxWKf66VnQLbdK/Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T13:19:28.470876Z"},"content_sha256":"8ae5402c1be88b1a7d4d9947804c6581424081149f6fd5beb62c3a2153818d60","schema_version":"1.0","event_id":"sha256:8ae5402c1be88b1a7d4d9947804c6581424081149f6fd5beb62c3a2153818d60"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:RH36Y2F37HPTAWT36CDBTROFHI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Two-Stage Adaptation of Large Language Models for Text Ranking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Dingkun Long, Longhui Zhang, Meishan Zhang, Min Zhang, Pengjun Xie, Yanzhao Zhang","submitted_at":"2023-11-28T12:04:19Z","abstract_excerpt":"Text ranking is a critical task in information retrieval. Recent advances in pre-trained language models (PLMs), especially large language models (LLMs), present new opportunities for applying them to text ranking. While supervised fine-tuning (SFT) with ranking data has been widely explored to better align PLMs with text ranking goals, previous studies have focused primarily on encoder-only and encoder-decoder PLMs. Research on leveraging decoder-only LLMs for text ranking remains scarce. An exception to this is RankLLaMA, which uses direct SFT to explore LLaMA's potential for text ranking. I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16720","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/2311.16720/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":"2026-07-05T08:25:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VRa8Zem+FqhlJ1ZlsftUHvI5pcWnYU7ITEZVbeDTtrE12xt8j3SQaJe3Kl4JYLxiYCbkb5I9K5gyt9eVI9w9DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-19T13:19:28.471609Z"},"content_sha256":"6e49ac5d6be269a7e322781c7ee72cfe336faf676806ad475116c49a4b63e285","schema_version":"1.0","event_id":"sha256:6e49ac5d6be269a7e322781c7ee72cfe336faf676806ad475116c49a4b63e285"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RH36Y2F37HPTAWT36CDBTROFHI/bundle.json","state_url":"https://pith.science/pith/RH36Y2F37HPTAWT36CDBTROFHI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RH36Y2F37HPTAWT36CDBTROFHI/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":"2026-08-19T13:19:28Z","links":{"resolver":"https://pith.science/pith/RH36Y2F37HPTAWT36CDBTROFHI","bundle":"https://pith.science/pith/RH36Y2F37HPTAWT36CDBTROFHI/bundle.json","state":"https://pith.science/pith/RH36Y2F37HPTAWT36CDBTROFHI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RH36Y2F37HPTAWT36CDBTROFHI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:RH36Y2F37HPTAWT36CDBTROFHI","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":"bacb66b6e3defbc8fa9ed4fee53dec4d7f1e84af9f3becad0aa69821394cd921","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-28T12:04:19Z","title_canon_sha256":"50e1251c0c357c1d5438fce8a97416506b57981cca367b00752b9280a2eb3eef"},"schema_version":"1.0","source":{"id":"2311.16720","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2311.16720","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"arxiv_version","alias_value":"2311.16720v3","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.16720","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"pith_short_12","alias_value":"RH36Y2F37HPT","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"pith_short_16","alias_value":"RH36Y2F37HPTAWT3","created_at":"2026-07-05T08:25:51Z"},{"alias_kind":"pith_short_8","alias_value":"RH36Y2F3","created_at":"2026-07-05T08:25:51Z"}],"graph_snapshots":[{"event_id":"sha256:6e49ac5d6be269a7e322781c7ee72cfe336faf676806ad475116c49a4b63e285","target":"graph","created_at":"2026-07-05T08:25:51Z","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/2311.16720/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Text ranking is a critical task in information retrieval. Recent advances in pre-trained language models (PLMs), especially large language models (LLMs), present new opportunities for applying them to text ranking. While supervised fine-tuning (SFT) with ranking data has been widely explored to better align PLMs with text ranking goals, previous studies have focused primarily on encoder-only and encoder-decoder PLMs. Research on leveraging decoder-only LLMs for text ranking remains scarce. An exception to this is RankLLaMA, which uses direct SFT to explore LLaMA's potential for text ranking. I","authors_text":"Dingkun Long, Longhui Zhang, Meishan Zhang, Min Zhang, Pengjun Xie, Yanzhao Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-28T12:04:19Z","title":"A Two-Stage Adaptation of Large Language Models for Text Ranking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.16720","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":"sha256:8ae5402c1be88b1a7d4d9947804c6581424081149f6fd5beb62c3a2153818d60","target":"record","created_at":"2026-07-05T08:25:51Z","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":"bacb66b6e3defbc8fa9ed4fee53dec4d7f1e84af9f3becad0aa69821394cd921","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IR","submitted_at":"2023-11-28T12:04:19Z","title_canon_sha256":"50e1251c0c357c1d5438fce8a97416506b57981cca367b00752b9280a2eb3eef"},"schema_version":"1.0","source":{"id":"2311.16720","kind":"arxiv","version":3}},"canonical_sha256":"89f7ec68bbf9df305a7bf08619c5c53a2e320a32f7c89470d9ed1b104b97ffeb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"89f7ec68bbf9df305a7bf08619c5c53a2e320a32f7c89470d9ed1b104b97ffeb","first_computed_at":"2026-07-05T08:25:51.171169Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:25:51.171169Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1/mIa1VLRlTP3bc7PblQ5HpkVns3HYC6pdme3EH+zfRbUJuWbX43wplfo91ikTYAUxEK7pNUy4F6ZSrdImxhDA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:25:51.171645Z","signed_message":"canonical_sha256_bytes"},"source_id":"2311.16720","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8ae5402c1be88b1a7d4d9947804c6581424081149f6fd5beb62c3a2153818d60","sha256:6e49ac5d6be269a7e322781c7ee72cfe336faf676806ad475116c49a4b63e285"],"state_sha256":"80c2d5236e8735c5623d7d6962add594446b4eede07cf300a2ab66cd938deb90"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3BTj/sWfv9KNj4wudY62MaAlLETDIzGvJTRHA6XoIKCNA6atx8mKU2nD4PwF83Ek1gQlnmJKQy63vriScOddCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-19T13:19:28.479512Z","bundle_sha256":"e016471d1e8b9f7e3fd4332d16a19c00454b5fd8403425b738e31448ef7bdc9c"}}