{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:XOQ5M3G65IC57CSL2H6DXY5QWF","short_pith_number":"pith:XOQ5M3G6","schema_version":"1.0","canonical_sha256":"bba1d66cdeea05df8a4bd1fc3be3b0b15c6dbaaf2a3e0a4091d42f0867755fd4","source":{"kind":"arxiv","id":"2103.10685","version":3},"attestation_state":"computed","paper":{"title":"Controllable Generation from Pre-trained Language Models via Inverse Prompting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Da Yin, Hongxia Yang, Jie Tang, Ming Ding, Qingyang Zhong, Xu Zou, Zhilin Yang","submitted_at":"2021-03-19T08:36:52Z","abstract_excerpt":"Large-scale pre-trained language models have demonstrated strong capabilities of generating realistic text. However, it remains challenging to control the generation results. Previous approaches such as prompting are far from sufficient, which limits the usage of language models. To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation. The core idea of inverse prompting is to use generated text to inversely predict the prompt during beam search, which enhances the relevance between the prompt and the generated text and provides better con"},"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":"2103.10685","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2021-03-19T08:36:52Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f682a3b267c049339cbc463b8dc064ed2b5a74648094ca5c794f348b28b4d655","abstract_canon_sha256":"115f88c457de2048e94a8a6bc26fc433ee03307b3b98848dc635d5e5b350dbe8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:30:10.275064Z","signature_b64":"deAL/b3V4ICXvDAY7q6CIrg4tzmhCP+2j5I1CVZTbFLww5+77rRjwugw7PDkMtsSdtCym/dwQqQhFGLHHRfgCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bba1d66cdeea05df8a4bd1fc3be3b0b15c6dbaaf2a3e0a4091d42f0867755fd4","last_reissued_at":"2026-07-05T03:30:10.274580Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:30:10.274580Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Controllable Generation from Pre-trained Language Models via Inverse Prompting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Da Yin, Hongxia Yang, Jie Tang, Ming Ding, Qingyang Zhong, Xu Zou, Zhilin Yang","submitted_at":"2021-03-19T08:36:52Z","abstract_excerpt":"Large-scale pre-trained language models have demonstrated strong capabilities of generating realistic text. However, it remains challenging to control the generation results. Previous approaches such as prompting are far from sufficient, which limits the usage of language models. To tackle this challenge, we propose an innovative method, inverse prompting, to better control text generation. The core idea of inverse prompting is to use generated text to inversely predict the prompt during beam search, which enhances the relevance between the prompt and the generated text and provides better con"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.10685","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/2103.10685/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":"2103.10685","created_at":"2026-07-05T03:30:10.274637+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.10685v3","created_at":"2026-07-05T03:30:10.274637+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.10685","created_at":"2026-07-05T03:30:10.274637+00:00"},{"alias_kind":"pith_short_12","alias_value":"XOQ5M3G65IC5","created_at":"2026-07-05T03:30:10.274637+00:00"},{"alias_kind":"pith_short_16","alias_value":"XOQ5M3G65IC57CSL","created_at":"2026-07-05T03:30:10.274637+00:00"},{"alias_kind":"pith_short_8","alias_value":"XOQ5M3G6","created_at":"2026-07-05T03:30:10.274637+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/XOQ5M3G65IC57CSL2H6DXY5QWF","json":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF.json","graph_json":"https://pith.science/api/pith-number/XOQ5M3G65IC57CSL2H6DXY5QWF/graph.json","events_json":"https://pith.science/api/pith-number/XOQ5M3G65IC57CSL2H6DXY5QWF/events.json","paper":"https://pith.science/paper/XOQ5M3G6"},"agent_actions":{"view_html":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF","download_json":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF.json","view_paper":"https://pith.science/paper/XOQ5M3G6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.10685&json=true","fetch_graph":"https://pith.science/api/pith-number/XOQ5M3G65IC57CSL2H6DXY5QWF/graph.json","fetch_events":"https://pith.science/api/pith-number/XOQ5M3G65IC57CSL2H6DXY5QWF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF/action/storage_attestation","attest_author":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF/action/author_attestation","sign_citation":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF/action/citation_signature","submit_replication":"https://pith.science/pith/XOQ5M3G65IC57CSL2H6DXY5QWF/action/replication_record"}},"created_at":"2026-07-05T03:30:10.274637+00:00","updated_at":"2026-07-05T03:30:10.274637+00:00"}