{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2PX667MSUO5MGG7TIKOP2PDLVM","short_pith_number":"pith:2PX667MS","schema_version":"1.0","canonical_sha256":"d3efef7d92a3bac31bf3429cfd3c6bab3f6733a4375c85b8101c9ed3c0aa9010","source":{"kind":"arxiv","id":"2206.06565","version":4},"attestation_state":"computed","paper":{"title":"LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Dimitris Papailiopoulos, Jy-yong Sohn, Kangwook Lee, Michael Gira, Ruisu Zhang, Shashank Rajput, Tuan Dinh, Yuchen Zeng, Ziqian Lin","submitted_at":"2022-06-14T02:41:41Z","abstract_excerpt":"Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to employ task-specific designs for input, output layers, and loss functions. For instance, it is possible to fine-tune an LM into an MNIST classifier by replacing the word embedding layer with an image patch embedding layer, the word token output layer with a 10-way output layer, and the word prediction loss with a 10-way classification loss, respectively. A natural question aris"},"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":"2206.06565","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-06-14T02:41:41Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"34d18e2d55340037026ba0f66d199a41ed3a0efe5b8444437f713d5711369296","abstract_canon_sha256":"53100755695718d56795f09efd2066a3fe8b98a9d14e272b2d4f818317ca25c4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:11:40.236922Z","signature_b64":"PIXdyXCrvYi2zkagRBpb8Ov60rc10kBlLSruB3GzY3v0Avu3pz1gXpNDbUwioZ5Ji8mo0VzxuY8BwuEqRjlgCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d3efef7d92a3bac31bf3429cfd3c6bab3f6733a4375c85b8101c9ed3c0aa9010","last_reissued_at":"2026-07-05T05:11:40.236426Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:11:40.236426Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Dimitris Papailiopoulos, Jy-yong Sohn, Kangwook Lee, Michael Gira, Ruisu Zhang, Shashank Rajput, Tuan Dinh, Yuchen Zeng, Ziqian Lin","submitted_at":"2022-06-14T02:41:41Z","abstract_excerpt":"Fine-tuning pretrained language models (LMs) without making any architectural changes has become a norm for learning various language downstream tasks. However, for non-language downstream tasks, a common practice is to employ task-specific designs for input, output layers, and loss functions. For instance, it is possible to fine-tune an LM into an MNIST classifier by replacing the word embedding layer with an image patch embedding layer, the word token output layer with a 10-way output layer, and the word prediction loss with a 10-way classification loss, respectively. A natural question aris"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.06565","kind":"arxiv","version":4},"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/2206.06565/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":"2206.06565","created_at":"2026-07-05T05:11:40.236489+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.06565v4","created_at":"2026-07-05T05:11:40.236489+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.06565","created_at":"2026-07-05T05:11:40.236489+00:00"},{"alias_kind":"pith_short_12","alias_value":"2PX667MSUO5M","created_at":"2026-07-05T05:11:40.236489+00:00"},{"alias_kind":"pith_short_16","alias_value":"2PX667MSUO5MGG7T","created_at":"2026-07-05T05:11:40.236489+00:00"},{"alias_kind":"pith_short_8","alias_value":"2PX667MS","created_at":"2026-07-05T05:11:40.236489+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.17582","citing_title":"Collaborative Large and Small Language Models for Accurate and Scalable Data Repair","ref_index":46,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13392","citing_title":"ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2207.05221","citing_title":"Language Models (Mostly) Know What They Know","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13392","citing_title":"ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold","ref_index":14,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM","json":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM.json","graph_json":"https://pith.science/api/pith-number/2PX667MSUO5MGG7TIKOP2PDLVM/graph.json","events_json":"https://pith.science/api/pith-number/2PX667MSUO5MGG7TIKOP2PDLVM/events.json","paper":"https://pith.science/paper/2PX667MS"},"agent_actions":{"view_html":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM","download_json":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM.json","view_paper":"https://pith.science/paper/2PX667MS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.06565&json=true","fetch_graph":"https://pith.science/api/pith-number/2PX667MSUO5MGG7TIKOP2PDLVM/graph.json","fetch_events":"https://pith.science/api/pith-number/2PX667MSUO5MGG7TIKOP2PDLVM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM/action/storage_attestation","attest_author":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM/action/author_attestation","sign_citation":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM/action/citation_signature","submit_replication":"https://pith.science/pith/2PX667MSUO5MGG7TIKOP2PDLVM/action/replication_record"}},"created_at":"2026-07-05T05:11:40.236489+00:00","updated_at":"2026-07-05T05:11:40.236489+00:00"}