{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:4MIBGGE2BF6ZXE57HEKTGYVF2Z","short_pith_number":"pith:4MIBGGE2","canonical_record":{"source":{"id":"2402.18385","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-28T15:05:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bf2312be565c942a22010420cfc02f38de21efc819f58744ece4b7e5f0c14569","abstract_canon_sha256":"f8f1b363b5007f7e28c0641cfe9691f7f2472563e93844b43677b76baec5c4f3"},"schema_version":"1.0"},"canonical_sha256":"e31013189a097d9b93bf39153362a5d67dc533c25d70dec0918f0b2a87134252","source":{"kind":"arxiv","id":"2402.18385","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.18385","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"arxiv_version","alias_value":"2402.18385v1","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18385","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"pith_short_12","alias_value":"4MIBGGE2BF6Z","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"pith_short_16","alias_value":"4MIBGGE2BF6ZXE57","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"pith_short_8","alias_value":"4MIBGGE2","created_at":"2026-07-05T07:50:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:4MIBGGE2BF6ZXE57HEKTGYVF2Z","target":"record","payload":{"canonical_record":{"source":{"id":"2402.18385","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-28T15:05:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"bf2312be565c942a22010420cfc02f38de21efc819f58744ece4b7e5f0c14569","abstract_canon_sha256":"f8f1b363b5007f7e28c0641cfe9691f7f2472563e93844b43677b76baec5c4f3"},"schema_version":"1.0"},"canonical_sha256":"e31013189a097d9b93bf39153362a5d67dc533c25d70dec0918f0b2a87134252","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:50:15.738028Z","signature_b64":"rDvcpW5IPdX0K3q4trUT8i0yXQY3x6XlyT2Vwx6tuOsGxa5d4ZYnoCzuBoqmqPBAIjBabxaKf7OYrWd5uSpKDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e31013189a097d9b93bf39153362a5d67dc533c25d70dec0918f0b2a87134252","last_reissued_at":"2026-07-05T07:50:15.737639Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:50:15.737639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.18385","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":"2026-07-05T07:50:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WYUSGl5VDcNAiye5M3PrQQFDSucDnssrz8BdfymPBZY/2lsczKyvl3BgYvS3BK9wmY1VxG9W19nJdx9R3pErBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:03:27.853477Z"},"content_sha256":"09031170b81d94844a3c7dff0d06ad6bf4c70039bc30cda2334a3999306af76f","schema_version":"1.0","event_id":"sha256:09031170b81d94844a3c7dff0d06ad6bf4c70039bc30cda2334a3999306af76f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:4MIBGGE2BF6ZXE57HEKTGYVF2Z","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Yiming Li, Zhao Zhang","submitted_at":"2024-02-28T15:05:43Z","abstract_excerpt":"Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the \"Conversational Multi-Doc QA\" challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large Language Models (LLMs). We first adapt LLMs to the task, then devise a hybrid training strategy to make the most of in-domain unlabeled data. Moreover, an advanced text embedding model is adopted to filter out potentially irrelevant docume"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18385","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/2402.18385/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":"2026-07-05T07:50:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hhITLpAQLGj927ut+IpccAfhNIOvh1QncMmVfOEGhwLbXQzAll2naP/GZ0SR1N1PT32gLIlxDVYWatc+lEF/DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T19:03:27.854087Z"},"content_sha256":"42fd8fb8cc889e10203eeb829d6cc4830204ecd71ef18554ef91495765efa154","schema_version":"1.0","event_id":"sha256:42fd8fb8cc889e10203eeb829d6cc4830204ecd71ef18554ef91495765efa154"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z/bundle.json","state_url":"https://pith.science/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z/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":"2026-08-08T19:03:27Z","links":{"resolver":"https://pith.science/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z","bundle":"https://pith.science/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z/bundle.json","state":"https://pith.science/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4MIBGGE2BF6ZXE57HEKTGYVF2Z/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:4MIBGGE2BF6ZXE57HEKTGYVF2Z","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":"f8f1b363b5007f7e28c0641cfe9691f7f2472563e93844b43677b76baec5c4f3","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-28T15:05:43Z","title_canon_sha256":"bf2312be565c942a22010420cfc02f38de21efc819f58744ece4b7e5f0c14569"},"schema_version":"1.0","source":{"id":"2402.18385","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.18385","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"arxiv_version","alias_value":"2402.18385v1","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.18385","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"pith_short_12","alias_value":"4MIBGGE2BF6Z","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"pith_short_16","alias_value":"4MIBGGE2BF6ZXE57","created_at":"2026-07-05T07:50:15Z"},{"alias_kind":"pith_short_8","alias_value":"4MIBGGE2","created_at":"2026-07-05T07:50:15Z"}],"graph_snapshots":[{"event_id":"sha256:42fd8fb8cc889e10203eeb829d6cc4830204ecd71ef18554ef91495765efa154","target":"graph","created_at":"2026-07-05T07:50:15Z","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/2402.18385/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the \"Conversational Multi-Doc QA\" challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large Language Models (LLMs). We first adapt LLMs to the task, then devise a hybrid training strategy to make the most of in-domain unlabeled data. Moreover, an advanced text embedding model is adopted to filter out potentially irrelevant docume","authors_text":"Yiming Li, Zhao Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-28T15:05:43Z","title":"The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.18385","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":"sha256:09031170b81d94844a3c7dff0d06ad6bf4c70039bc30cda2334a3999306af76f","target":"record","created_at":"2026-07-05T07:50:15Z","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":"f8f1b363b5007f7e28c0641cfe9691f7f2472563e93844b43677b76baec5c4f3","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-02-28T15:05:43Z","title_canon_sha256":"bf2312be565c942a22010420cfc02f38de21efc819f58744ece4b7e5f0c14569"},"schema_version":"1.0","source":{"id":"2402.18385","kind":"arxiv","version":1}},"canonical_sha256":"e31013189a097d9b93bf39153362a5d67dc533c25d70dec0918f0b2a87134252","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e31013189a097d9b93bf39153362a5d67dc533c25d70dec0918f0b2a87134252","first_computed_at":"2026-07-05T07:50:15.737639Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:50:15.737639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rDvcpW5IPdX0K3q4trUT8i0yXQY3x6XlyT2Vwx6tuOsGxa5d4ZYnoCzuBoqmqPBAIjBabxaKf7OYrWd5uSpKDA==","signature_status":"signed_v1","signed_at":"2026-07-05T07:50:15.738028Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.18385","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:09031170b81d94844a3c7dff0d06ad6bf4c70039bc30cda2334a3999306af76f","sha256:42fd8fb8cc889e10203eeb829d6cc4830204ecd71ef18554ef91495765efa154"],"state_sha256":"5c9a3c9f253b01c0f0a84abb8f0a63599d320d43a2db7745f4bfc1c31ad79509"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PviEMua3xkC8e4cW4RLrxeCGdsnEcVc5GftaOo2qJpuC+RZ2dFEZbtFE8a0qfvi7pab9UAaJao0t8mjXQ64XCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T19:03:27.859144Z","bundle_sha256":"07e0fd1e8c68586b16a47834f5cbcd0f425ca753d451f44cd826fb169a1a5807"}}