{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:J46UMLGXC2XH42URTUKRM42QIJ","short_pith_number":"pith:J46UMLGX","schema_version":"1.0","canonical_sha256":"4f3d462cd716ae7e6a919d151673504263b1ffa8817c3d47431eae7f1a4c70f1","source":{"kind":"arxiv","id":"2210.02969","version":4},"attestation_state":"computed","paper":{"title":"Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Doyoung Kim, Joel Jang, Joongbo Shin, Minjoon Seo, Seonghyeon Ye","submitted_at":"2022-10-06T15:00:47Z","abstract_excerpt":"Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance. However, meta-trained LMs still struggle to generalize to challenging tasks containing novel labels unseen during meta-training. In this paper, we propose Flipped Learning, an alternative method of meta-training which trains the LM to generate the task instruction given the input instance and label. During inference, the LM trained with Flipped Learning, ref"},"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":"2210.02969","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-10-06T15:00:47Z","cross_cats_sorted":[],"title_canon_sha256":"83f38fedf760b42970f061b35a4bfe69280bc8687fc06c8c00753b4c0f66a679","abstract_canon_sha256":"090fcc0b385471cf1ad860f65518521eb64c5b3ae18aabfd4525f9800359e8fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:17:40.361622Z","signature_b64":"cgy1suEWvmNrcE0GrHQLDAhBFcxPDakT3xTXM6JV546MZmkjelabRQBmsyXHllDYjeNJ+7JZ36C8uTFIz77NDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f3d462cd716ae7e6a919d151673504263b1ffa8817c3d47431eae7f1a4c70f1","last_reissued_at":"2026-07-05T06:17:40.361060Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:17:40.361060Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Doyoung Kim, Joel Jang, Joongbo Shin, Minjoon Seo, Seonghyeon Ye","submitted_at":"2022-10-06T15:00:47Z","abstract_excerpt":"Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance, has improved the zero-shot task generalization performance. However, meta-trained LMs still struggle to generalize to challenging tasks containing novel labels unseen during meta-training. In this paper, we propose Flipped Learning, an alternative method of meta-training which trains the LM to generate the task instruction given the input instance and label. During inference, the LM trained with Flipped Learning, ref"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02969","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/2210.02969/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":"2210.02969","created_at":"2026-07-05T06:17:40.361126+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.02969v4","created_at":"2026-07-05T06:17:40.361126+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02969","created_at":"2026-07-05T06:17:40.361126+00:00"},{"alias_kind":"pith_short_12","alias_value":"J46UMLGXC2XH","created_at":"2026-07-05T06:17:40.361126+00:00"},{"alias_kind":"pith_short_16","alias_value":"J46UMLGXC2XH42UR","created_at":"2026-07-05T06:17:40.361126+00:00"},{"alias_kind":"pith_short_8","alias_value":"J46UMLGX","created_at":"2026-07-05T06:17:40.361126+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.09659","citing_title":"LAS: Loss-less ANN-SNN Conversion for Fully Spike-Driven Large Language Models","ref_index":43,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ","json":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ.json","graph_json":"https://pith.science/api/pith-number/J46UMLGXC2XH42URTUKRM42QIJ/graph.json","events_json":"https://pith.science/api/pith-number/J46UMLGXC2XH42URTUKRM42QIJ/events.json","paper":"https://pith.science/paper/J46UMLGX"},"agent_actions":{"view_html":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ","download_json":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ.json","view_paper":"https://pith.science/paper/J46UMLGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.02969&json=true","fetch_graph":"https://pith.science/api/pith-number/J46UMLGXC2XH42URTUKRM42QIJ/graph.json","fetch_events":"https://pith.science/api/pith-number/J46UMLGXC2XH42URTUKRM42QIJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ/action/storage_attestation","attest_author":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ/action/author_attestation","sign_citation":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ/action/citation_signature","submit_replication":"https://pith.science/pith/J46UMLGXC2XH42URTUKRM42QIJ/action/replication_record"}},"created_at":"2026-07-05T06:17:40.361126+00:00","updated_at":"2026-07-05T06:17:40.361126+00:00"}