{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KHIDZ4L5E5DN3JV6LTN5ZT5NNY","short_pith_number":"pith:KHIDZ4L5","schema_version":"1.0","canonical_sha256":"51d03cf17d2746dda6be5cdbdccfad6e237be18862f5e734083c88e7d3dbe77d","source":{"kind":"arxiv","id":"2305.05252","version":5},"attestation_state":"computed","paper":{"title":"Distilling Script Knowledge from Large Language Models for Constrained Language Planning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Charles Robert Jankowski, Deqing Yang, Jiangjie Chen, Siyu Yuan, Soham Shah, Xuyang Ge, Yanghua Xiao, Ziquan Fu","submitted_at":"2023-05-09T08:19:32Z","abstract_excerpt":"In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for abstract goals of stereotypical activities (e.g., \"make a cake\"), but leaves more specific goals with multi-facet constraints understudied (e.g., \"make a cake for diabetics\"). In this paper, we define the task of constrained language planning for the first time. We propose an overgenerate-then-filter approach to improve large language models (LLMs) on this task, and use it to distill a novel constrained lang"},"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":"2305.05252","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-09T08:19:32Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"75728b85cf8296d55287211aad8a4267793c14e513dc848c3a935a6294378665","abstract_canon_sha256":"d8ce03100db1c28e440ce59139689d26877431e9b5f78c8135358abf86c932c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:10.112400Z","signature_b64":"QcM/LKZkb9UlvnfTjtTDoY83Sg+CjEDQUZL5CjGR6amfSMJutLFQw2b2Oyb6+YjZlyAX8jre8Wi+kxrIlu3PBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"51d03cf17d2746dda6be5cdbdccfad6e237be18862f5e734083c88e7d3dbe77d","last_reissued_at":"2026-07-05T06:14:10.111950Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:10.111950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Distilling Script Knowledge from Large Language Models for Constrained Language Planning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Charles Robert Jankowski, Deqing Yang, Jiangjie Chen, Siyu Yuan, Soham Shah, Xuyang Ge, Yanghua Xiao, Ziquan Fu","submitted_at":"2023-05-09T08:19:32Z","abstract_excerpt":"In everyday life, humans often plan their actions by following step-by-step instructions in the form of goal-oriented scripts. Previous work has exploited language models (LMs) to plan for abstract goals of stereotypical activities (e.g., \"make a cake\"), but leaves more specific goals with multi-facet constraints understudied (e.g., \"make a cake for diabetics\"). In this paper, we define the task of constrained language planning for the first time. We propose an overgenerate-then-filter approach to improve large language models (LLMs) on this task, and use it to distill a novel constrained lang"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.05252","kind":"arxiv","version":5},"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/2305.05252/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":"2305.05252","created_at":"2026-07-05T06:14:10.112007+00:00"},{"alias_kind":"arxiv_version","alias_value":"2305.05252v5","created_at":"2026-07-05T06:14:10.112007+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.05252","created_at":"2026-07-05T06:14:10.112007+00:00"},{"alias_kind":"pith_short_12","alias_value":"KHIDZ4L5E5DN","created_at":"2026-07-05T06:14:10.112007+00:00"},{"alias_kind":"pith_short_16","alias_value":"KHIDZ4L5E5DN3JV6","created_at":"2026-07-05T06:14:10.112007+00:00"},{"alias_kind":"pith_short_8","alias_value":"KHIDZ4L5","created_at":"2026-07-05T06:14:10.112007+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.15715","citing_title":"Task Scheduling for Efficient Inference of Large Language Models on Single Moderate GPU Systems","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY","json":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY.json","graph_json":"https://pith.science/api/pith-number/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/graph.json","events_json":"https://pith.science/api/pith-number/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/events.json","paper":"https://pith.science/paper/KHIDZ4L5"},"agent_actions":{"view_html":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY","download_json":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY.json","view_paper":"https://pith.science/paper/KHIDZ4L5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2305.05252&json=true","fetch_graph":"https://pith.science/api/pith-number/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/graph.json","fetch_events":"https://pith.science/api/pith-number/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/action/storage_attestation","attest_author":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/action/author_attestation","sign_citation":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/action/citation_signature","submit_replication":"https://pith.science/pith/KHIDZ4L5E5DN3JV6LTN5ZT5NNY/action/replication_record"}},"created_at":"2026-07-05T06:14:10.112007+00:00","updated_at":"2026-07-05T06:14:10.112007+00:00"}