{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:V36RWAUPGHTH22LGE3M2SJC6LF","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3ca92cf3a427b4ec26bdb8d4be6d667e4771cca841a43f85ecb8dfe841c98c8d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-23T17:28:21Z","title_canon_sha256":"5d4ba9d7a8372ad6f7b7bea3ceaf24ff1282b54878ac69a30c5333cafb50e628"},"schema_version":"1.0","source":{"id":"2308.12261","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2308.12261","created_at":"2026-07-05T06:44:02Z"},{"alias_kind":"arxiv_version","alias_value":"2308.12261v1","created_at":"2026-07-05T06:44:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12261","created_at":"2026-07-05T06:44:02Z"},{"alias_kind":"pith_short_12","alias_value":"V36RWAUPGHTH","created_at":"2026-07-05T06:44:02Z"},{"alias_kind":"pith_short_16","alias_value":"V36RWAUPGHTH22LG","created_at":"2026-07-05T06:44:02Z"},{"alias_kind":"pith_short_8","alias_value":"V36RWAUP","created_at":"2026-07-05T06:44:02Z"}],"graph_snapshots":[{"event_id":"sha256:2ccba93dcfd3c107c1b2ce784f915819d00c1fc33bbab8b121baa2c9b7622937","target":"graph","created_at":"2026-07-05T06:44:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2308.12261/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) enable system builders today to create competent NLP systems through prompting, where they only need to describe the task in natural language and provide a few examples. However, in other ways, LLMs are a step backward from traditional special-purpose NLP models; they require extensive computational resources for deployment and can be gated behind APIs. In this paper, we propose Prompt2Model, a general-purpose method that takes a natural language task description like the prompts provided to LLMs, and uses it to train a special-purpose model that is conducive to de","authors_text":"Amanda Bertsch, Chenyang Zhao, Graham Neubig, Tongshuang Wu, Vijay Viswanathan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-23T17:28:21Z","title":"Prompt2Model: Generating Deployable Models from Natural Language Instructions"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12261","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:0ac0b865ddaebdb66e6b31cee8c0000a25fed0b1e1df3fa4a69d296633280c21","target":"record","created_at":"2026-07-05T06:44:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3ca92cf3a427b4ec26bdb8d4be6d667e4771cca841a43f85ecb8dfe841c98c8d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-08-23T17:28:21Z","title_canon_sha256":"5d4ba9d7a8372ad6f7b7bea3ceaf24ff1282b54878ac69a30c5333cafb50e628"},"schema_version":"1.0","source":{"id":"2308.12261","kind":"arxiv","version":1}},"canonical_sha256":"aefd1b028f31e67d696626d9a9245e595dd971f4cc64958021b37631692d3d24","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aefd1b028f31e67d696626d9a9245e595dd971f4cc64958021b37631692d3d24","first_computed_at":"2026-07-05T06:44:02.108778Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:44:02.108778Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"apBWQHmfvW6QD/919kti2Om8/7sCxWmLnJkuNcxM6tJqTo/cYsRLILVslFN1f9FMKAZCj/JKkibOEjy9HOhIDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:44:02.109269Z","signed_message":"canonical_sha256_bytes"},"source_id":"2308.12261","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0ac0b865ddaebdb66e6b31cee8c0000a25fed0b1e1df3fa4a69d296633280c21","sha256:2ccba93dcfd3c107c1b2ce784f915819d00c1fc33bbab8b121baa2c9b7622937"],"state_sha256":"4ea07562de6f12f9f1263505ba8da6f500807a8a6c3775579ce71993313274cf"}