{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:3YDGTWZ7ACI5XXBOGMMU3HMZY3","short_pith_number":"pith:3YDGTWZ7","schema_version":"1.0","canonical_sha256":"de0669db3f0091dbdc2e33194d9d99c6dab9283571e164a0e3aa88b3b5c3fbf2","source":{"kind":"arxiv","id":"2212.09512","version":3},"attestation_state":"computed","paper":{"title":"Rethinking Label Smoothing on Multi-hop Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hang Yan, Xiannian Hu, Xinyu Zhang, Xipeng Qiu, Xuanjing Huang, Yiguang Wu, Yuxin Wang, Zhangyue Yin, Zhao Cao","submitted_at":"2022-12-19T14:48:08Z","abstract_excerpt":"Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence prediction, and answer span extraction. In this work, we analyze the primary factors limiting the performance of multi-hop reasoning and introduce label smoothing into the MHQA task. This is aimed at enhancing the generalization capabilities of MHQA systems and mitigating overfitting of answer spans and reasoning paths in training set. We propose a novel label smoothing technique, F1 Smoothing, which incorporates uncertainty"},"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":"2212.09512","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-12-19T14:48:08Z","cross_cats_sorted":[],"title_canon_sha256":"ce612862162fecdcc374cf104716932e601db105ed89dba12cf415477b186185","abstract_canon_sha256":"75c1f2cbc2e7d59289d766410dfd5682a7adfeb0a899f9efb292192ba8eedbe9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:23.257926Z","signature_b64":"kz7tCNnG03m0WYiFQH6YN1aYrAyy2YNqNCJXA8GLhsJdXFWg43ORMCI0xLN2JbS2EcWy6Dhrg4IIRsIcS2+rAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de0669db3f0091dbdc2e33194d9d99c6dab9283571e164a0e3aa88b3b5c3fbf2","last_reissued_at":"2026-07-05T07:23:23.257496Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:23.257496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Label Smoothing on Multi-hop Question Answering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Hang Yan, Xiannian Hu, Xinyu Zhang, Xipeng Qiu, Xuanjing Huang, Yiguang Wu, Yuxin Wang, Zhangyue Yin, Zhao Cao","submitted_at":"2022-12-19T14:48:08Z","abstract_excerpt":"Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence prediction, and answer span extraction. In this work, we analyze the primary factors limiting the performance of multi-hop reasoning and introduce label smoothing into the MHQA task. This is aimed at enhancing the generalization capabilities of MHQA systems and mitigating overfitting of answer spans and reasoning paths in training set. We propose a novel label smoothing technique, F1 Smoothing, which incorporates uncertainty"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.09512","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/2212.09512/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":"2212.09512","created_at":"2026-07-05T07:23:23.257556+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.09512v3","created_at":"2026-07-05T07:23:23.257556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.09512","created_at":"2026-07-05T07:23:23.257556+00:00"},{"alias_kind":"pith_short_12","alias_value":"3YDGTWZ7ACI5","created_at":"2026-07-05T07:23:23.257556+00:00"},{"alias_kind":"pith_short_16","alias_value":"3YDGTWZ7ACI5XXBO","created_at":"2026-07-05T07:23:23.257556+00:00"},{"alias_kind":"pith_short_8","alias_value":"3YDGTWZ7","created_at":"2026-07-05T07:23:23.257556+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.14515","citing_title":"Relational Programming with Foundation Models","ref_index":56,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3","json":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3.json","graph_json":"https://pith.science/api/pith-number/3YDGTWZ7ACI5XXBOGMMU3HMZY3/graph.json","events_json":"https://pith.science/api/pith-number/3YDGTWZ7ACI5XXBOGMMU3HMZY3/events.json","paper":"https://pith.science/paper/3YDGTWZ7"},"agent_actions":{"view_html":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3","download_json":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3.json","view_paper":"https://pith.science/paper/3YDGTWZ7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.09512&json=true","fetch_graph":"https://pith.science/api/pith-number/3YDGTWZ7ACI5XXBOGMMU3HMZY3/graph.json","fetch_events":"https://pith.science/api/pith-number/3YDGTWZ7ACI5XXBOGMMU3HMZY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3/action/storage_attestation","attest_author":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3/action/author_attestation","sign_citation":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3/action/citation_signature","submit_replication":"https://pith.science/pith/3YDGTWZ7ACI5XXBOGMMU3HMZY3/action/replication_record"}},"created_at":"2026-07-05T07:23:23.257556+00:00","updated_at":"2026-07-05T07:23:23.257556+00:00"}