{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LVV2LZQ2PPSVT2AZWVNA5SSXEI","short_pith_number":"pith:LVV2LZQ2","schema_version":"1.0","canonical_sha256":"5d6ba5e61a7be559e819b55a0eca57220258639485d1704ece31f2a2e013735d","source":{"kind":"arxiv","id":"2212.10539","version":1},"attestation_state":"computed","paper":{"title":"Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ari Holtzman, Hila Gonen, Luke Zettlemoyer, Weijia Shi, Xiaochuang Han, Yulia Tsvetkov","submitted_at":"2022-12-20T18:47:13Z","abstract_excerpt":"Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are natural language. In this paper, we investigate common attributes shared by effective prompts. We first propose a human readable prompt tuning method (F LUENT P ROMPT) based on Langevin dynamics that incorporates a fluency constraint to find a diver"},"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":"2212.10539","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2022-12-20T18:47:13Z","cross_cats_sorted":[],"title_canon_sha256":"fefc45d2e338d50875f607ad79f106d05eb92f39bd1051ed5e0998896b50e619","abstract_canon_sha256":"dbccf184871851c6968afd8c9d11c1b0e24d83f7ad53418ecff565b67ad22a2b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:27:08.552205Z","signature_b64":"FOb2qR8AGZJVcjCC5J/4nPieENumPX6fIn0kx9APWWrdOraLFJafO/oTkG5NBIFtyVUVrHN0BLi8MYY5DdJzAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d6ba5e61a7be559e819b55a0eca57220258639485d1704ece31f2a2e013735d","last_reissued_at":"2026-07-05T05:27:08.551809Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:27:08.551809Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Toward Human Readable Prompt Tuning: Kubrick's The Shining is a good movie, and a good prompt too?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ari Holtzman, Hila Gonen, Luke Zettlemoyer, Weijia Shi, Xiaochuang Han, Yulia Tsvetkov","submitted_at":"2022-12-20T18:47:13Z","abstract_excerpt":"Large language models can perform new tasks in a zero-shot fashion, given natural language prompts that specify the desired behavior. Such prompts are typically hand engineered, but can also be learned with gradient-based methods from labeled data. However, it is underexplored what factors make the prompts effective, especially when the prompts are natural language. In this paper, we investigate common attributes shared by effective prompts. We first propose a human readable prompt tuning method (F LUENT P ROMPT) based on Langevin dynamics that incorporates a fluency constraint to find a diver"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.10539","kind":"arxiv","version":1},"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/2212.10539/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":"2212.10539","created_at":"2026-07-05T05:27:08.551864+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.10539v1","created_at":"2026-07-05T05:27:08.551864+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.10539","created_at":"2026-07-05T05:27:08.551864+00:00"},{"alias_kind":"pith_short_12","alias_value":"LVV2LZQ2PPSV","created_at":"2026-07-05T05:27:08.551864+00:00"},{"alias_kind":"pith_short_16","alias_value":"LVV2LZQ2PPSVT2AZ","created_at":"2026-07-05T05:27:08.551864+00:00"},{"alias_kind":"pith_short_8","alias_value":"LVV2LZQ2","created_at":"2026-07-05T05:27:08.551864+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2310.11324","citing_title":"Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting","ref_index":51,"is_internal_anchor":false},{"citing_arxiv_id":"2309.08532","citing_title":"EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers","ref_index":119,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI","json":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI.json","graph_json":"https://pith.science/api/pith-number/LVV2LZQ2PPSVT2AZWVNA5SSXEI/graph.json","events_json":"https://pith.science/api/pith-number/LVV2LZQ2PPSVT2AZWVNA5SSXEI/events.json","paper":"https://pith.science/paper/LVV2LZQ2"},"agent_actions":{"view_html":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI","download_json":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI.json","view_paper":"https://pith.science/paper/LVV2LZQ2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.10539&json=true","fetch_graph":"https://pith.science/api/pith-number/LVV2LZQ2PPSVT2AZWVNA5SSXEI/graph.json","fetch_events":"https://pith.science/api/pith-number/LVV2LZQ2PPSVT2AZWVNA5SSXEI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI/action/storage_attestation","attest_author":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI/action/author_attestation","sign_citation":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI/action/citation_signature","submit_replication":"https://pith.science/pith/LVV2LZQ2PPSVT2AZWVNA5SSXEI/action/replication_record"}},"created_at":"2026-07-05T05:27:08.551864+00:00","updated_at":"2026-07-05T05:27:08.551864+00:00"}