{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:24CCHCTXDKXJBSBUS2SPXAI52H","short_pith_number":"pith:24CCHCTX","schema_version":"1.0","canonical_sha256":"d704238a771aae90c83496a4fb811dd1e8ba5b892fb3b4a5fd596722b7afaeae","source":{"kind":"arxiv","id":"2205.10744","version":1},"attestation_state":"computed","paper":{"title":"All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Adam D. Lelkes, Cong Yu, Jialu Liu, Jiawei Han, Jiaxin Huang, Tianqi Liu","submitted_at":"2022-05-22T05:16:03Z","abstract_excerpt":"Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks. In real industrial applications such as web content classification, multiple classification tasks are predicted from the same input text such as a web article. However, at the serving time, the existing multitask transformer models such as prompt or adaptor based approaches need to conduct N forward passes for N tasks with O(N) computation cost. To tackle this problem, we propose a scalable method that can achieve stronger performance with close to O(1) c"},"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":"2205.10744","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2022-05-22T05:16:03Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c476f909bab75569c46e29341d91dc3c95d28870a367d56b9d6dac78122a4cc5","abstract_canon_sha256":"1b5892dfb0651944c2675c451eb6d6b71a915de2e3b026f27cee581186f2a18a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:25:23.143595Z","signature_b64":"7saXRQJndszOj1e/bAoQsy3jyOyF5csqimjHw33kw23pHGqnkigZ9ONwCt4BQL1wjQy0lASFnrixEOFBqoqjDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d704238a771aae90c83496a4fb811dd1e8ba5b892fb3b4a5fd596722b7afaeae","last_reissued_at":"2026-07-05T04:25:23.143129Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:25:23.143129Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"All Birds with One Stone: Multi-task Text Classification for Efficient Inference with One Forward Pass","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Adam D. Lelkes, Cong Yu, Jialu Liu, Jiawei Han, Jiaxin Huang, Tianqi Liu","submitted_at":"2022-05-22T05:16:03Z","abstract_excerpt":"Multi-Task Learning (MTL) models have shown their robustness, effectiveness, and efficiency for transferring learned knowledge across tasks. In real industrial applications such as web content classification, multiple classification tasks are predicted from the same input text such as a web article. However, at the serving time, the existing multitask transformer models such as prompt or adaptor based approaches need to conduct N forward passes for N tasks with O(N) computation cost. To tackle this problem, we propose a scalable method that can achieve stronger performance with close to O(1) c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2205.10744","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/2205.10744/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":"2205.10744","created_at":"2026-07-05T04:25:23.143186+00:00"},{"alias_kind":"arxiv_version","alias_value":"2205.10744v1","created_at":"2026-07-05T04:25:23.143186+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2205.10744","created_at":"2026-07-05T04:25:23.143186+00:00"},{"alias_kind":"pith_short_12","alias_value":"24CCHCTXDKXJ","created_at":"2026-07-05T04:25:23.143186+00:00"},{"alias_kind":"pith_short_16","alias_value":"24CCHCTXDKXJBSBU","created_at":"2026-07-05T04:25:23.143186+00:00"},{"alias_kind":"pith_short_8","alias_value":"24CCHCTX","created_at":"2026-07-05T04:25:23.143186+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H","json":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H.json","graph_json":"https://pith.science/api/pith-number/24CCHCTXDKXJBSBUS2SPXAI52H/graph.json","events_json":"https://pith.science/api/pith-number/24CCHCTXDKXJBSBUS2SPXAI52H/events.json","paper":"https://pith.science/paper/24CCHCTX"},"agent_actions":{"view_html":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H","download_json":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H.json","view_paper":"https://pith.science/paper/24CCHCTX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2205.10744&json=true","fetch_graph":"https://pith.science/api/pith-number/24CCHCTXDKXJBSBUS2SPXAI52H/graph.json","fetch_events":"https://pith.science/api/pith-number/24CCHCTXDKXJBSBUS2SPXAI52H/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H/action/timestamp_anchor","attest_storage":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H/action/storage_attestation","attest_author":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H/action/author_attestation","sign_citation":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H/action/citation_signature","submit_replication":"https://pith.science/pith/24CCHCTXDKXJBSBUS2SPXAI52H/action/replication_record"}},"created_at":"2026-07-05T04:25:23.143186+00:00","updated_at":"2026-07-05T04:25:23.143186+00:00"}