{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:VLRD4EUUOWPXLBCPCNXUNY7TSU","short_pith_number":"pith:VLRD4EUU","schema_version":"1.0","canonical_sha256":"aae23e1294759f75844f136f46e3f39527c1b86b0ab9992a7d201c896d66b139","source":{"kind":"arxiv","id":"2201.05880","version":1},"attestation_state":"computed","paper":{"title":"Reasoning over Hybrid Chain for Table-and-Text Open Domain QA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiahai Wang, Jian Yin, Junjie Huang, Ming Zhou, Nan Duan, Qian Liu, Wanjun Zhong","submitted_at":"2022-01-15T16:11:55Z","abstract_excerpt":"Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method construc"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2201.05880","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-01-15T16:11:55Z","cross_cats_sorted":[],"title_canon_sha256":"13dddd0bb79a2bb2a02b607511c505aaa6789f38af2bc82ea68c2784dc169fd4","abstract_canon_sha256":"1af81845bd5ad47ea38c5b6fdea6e44b019c2a1826670e6c7ee7ed662d5b5b85"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:52.555444Z","signature_b64":"antGSNZ1lOwB/zYb0WNvAHnEqXhNhl6cJw7n3akMBTfgP8YLFlwuEI3BhBVQT2Yy17KSdg81CphJwUsiAhUECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aae23e1294759f75844f136f46e3f39527c1b86b0ab9992a7d201c896d66b139","last_reissued_at":"2026-07-05T03:48:52.554996Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:52.554996Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reasoning over Hybrid Chain for Table-and-Text Open Domain QA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jiahai Wang, Jian Yin, Junjie Huang, Ming Zhou, Nan Duan, Qian Liu, Wanjun Zhong","submitted_at":"2022-01-15T16:11:55Z","abstract_excerpt":"Tabular and textual question answering requires systems to perform reasoning over heterogeneous information, considering table structure, and the connections among table and text. In this paper, we propose a ChAin-centric Reasoning and Pre-training framework (CARP). CARP utilizes hybrid chain to model the explicit intermediate reasoning process across table and text for question answering. We also propose a novel chain-centric pre-training method, to enhance the pre-trained model in identifying the cross-modality reasoning process and alleviating the data sparsity problem. This method construc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.05880","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/2201.05880/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2201.05880","created_at":"2026-07-05T03:48:52.555052+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.05880v1","created_at":"2026-07-05T03:48:52.555052+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.05880","created_at":"2026-07-05T03:48:52.555052+00:00"},{"alias_kind":"pith_short_12","alias_value":"VLRD4EUUOWPX","created_at":"2026-07-05T03:48:52.555052+00:00"},{"alias_kind":"pith_short_16","alias_value":"VLRD4EUUOWPXLBCP","created_at":"2026-07-05T03:48:52.555052+00:00"},{"alias_kind":"pith_short_8","alias_value":"VLRD4EUU","created_at":"2026-07-05T03:48:52.555052+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2501.17767","citing_title":"Hybrid Graphs for Table-and-Text based Question Answering using LLMs","ref_index":44,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU","json":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU.json","graph_json":"https://pith.science/api/pith-number/VLRD4EUUOWPXLBCPCNXUNY7TSU/graph.json","events_json":"https://pith.science/api/pith-number/VLRD4EUUOWPXLBCPCNXUNY7TSU/events.json","paper":"https://pith.science/paper/VLRD4EUU"},"agent_actions":{"view_html":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU","download_json":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU.json","view_paper":"https://pith.science/paper/VLRD4EUU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.05880&json=true","fetch_graph":"https://pith.science/api/pith-number/VLRD4EUUOWPXLBCPCNXUNY7TSU/graph.json","fetch_events":"https://pith.science/api/pith-number/VLRD4EUUOWPXLBCPCNXUNY7TSU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU/action/storage_attestation","attest_author":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU/action/author_attestation","sign_citation":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU/action/citation_signature","submit_replication":"https://pith.science/pith/VLRD4EUUOWPXLBCPCNXUNY7TSU/action/replication_record"}},"created_at":"2026-07-05T03:48:52.555052+00:00","updated_at":"2026-07-05T03:48:52.555052+00:00"}