{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:L7IM76TYL3OPNRBTNPNJ2YZSCH","short_pith_number":"pith:L7IM76TY","schema_version":"1.0","canonical_sha256":"5fd0cffa785edcf6c4336bda9d633211cd5d5251a588b13e48c803701c2141c8","source":{"kind":"arxiv","id":"2210.12374","version":1},"attestation_state":"computed","paper":{"title":"ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dragomir Radev, Linyong Nan, Rui Zhang, Yilun Zhao, Zhenting Qi","submitted_at":"2022-10-22T07:04:02Z","abstract_excerpt":"Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction"},"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":"2210.12374","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-22T07:04:02Z","cross_cats_sorted":[],"title_canon_sha256":"1661f7c7cfbc6c07f81bde02659b40a0d9dd93dbca0c16a1d2db05cbb50ab293","abstract_canon_sha256":"3f44731c8830bd2de2be1946b61d83f648ccaa89f817bffff94318ec81d6d292"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:09:27.410065Z","signature_b64":"+v44zLMfSrdw5EAWvfWqbFaoKlxPpHPyUtQTF6LlLKxjVCVoBKrqq6JX6NXuR+13ZDEmtFAYt9TrzfUtnTwBAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5fd0cffa785edcf6c4336bda9d633211cd5d5251a588b13e48c803701c2141c8","last_reissued_at":"2026-07-05T05:09:27.409400Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:09:27.409400Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Dragomir Radev, Linyong Nan, Rui Zhang, Yilun Zhao, Zhenting Qi","submitted_at":"2022-10-22T07:04:02Z","abstract_excerpt":"Reasoning over tabular data requires both table structure understanding and a broad set of table reasoning skills. Current models with table-specific architectures and pre-training methods perform well on understanding table structures, but they still struggle with tasks that require various table reasoning skills. In this work, we develop ReasTAP to show that high-level table reasoning skills can be injected into models during pre-training without a complex table-specific architecture design. We define 7 table reasoning skills, such as numerical operation, temporal comparison, and conjunction"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.12374","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/2210.12374/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":"2210.12374","created_at":"2026-07-05T05:09:27.409465+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.12374v1","created_at":"2026-07-05T05:09:27.409465+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.12374","created_at":"2026-07-05T05:09:27.409465+00:00"},{"alias_kind":"pith_short_12","alias_value":"L7IM76TYL3OP","created_at":"2026-07-05T05:09:27.409465+00:00"},{"alias_kind":"pith_short_16","alias_value":"L7IM76TYL3OPNRBT","created_at":"2026-07-05T05:09:27.409465+00:00"},{"alias_kind":"pith_short_8","alias_value":"L7IM76TY","created_at":"2026-07-05T05:09:27.409465+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.18676","citing_title":"Utilizing Training Data to Improve LLM Reasoning for Tabular Understanding","ref_index":42,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH","json":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH.json","graph_json":"https://pith.science/api/pith-number/L7IM76TYL3OPNRBTNPNJ2YZSCH/graph.json","events_json":"https://pith.science/api/pith-number/L7IM76TYL3OPNRBTNPNJ2YZSCH/events.json","paper":"https://pith.science/paper/L7IM76TY"},"agent_actions":{"view_html":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH","download_json":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH.json","view_paper":"https://pith.science/paper/L7IM76TY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.12374&json=true","fetch_graph":"https://pith.science/api/pith-number/L7IM76TYL3OPNRBTNPNJ2YZSCH/graph.json","fetch_events":"https://pith.science/api/pith-number/L7IM76TYL3OPNRBTNPNJ2YZSCH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH/action/storage_attestation","attest_author":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH/action/author_attestation","sign_citation":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH/action/citation_signature","submit_replication":"https://pith.science/pith/L7IM76TYL3OPNRBTNPNJ2YZSCH/action/replication_record"}},"created_at":"2026-07-05T05:09:27.409465+00:00","updated_at":"2026-07-05T05:09:27.409465+00:00"}