{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:QCP2XNI7JL54LVLT5ZRJIU6LMA","short_pith_number":"pith:QCP2XNI7","schema_version":"1.0","canonical_sha256":"809fabb51f4afbc5d573ee629453cb6032c888a6318b0a5d03cfc7b45e3c3ef6","source":{"kind":"arxiv","id":"2505.07886","version":1},"attestation_state":"computed","paper":{"title":"PLHF: Prompt Optimization with Few-Shot Human Feedback","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chun-Pai Yang, Kan Zheng, Shou-De Lin","submitted_at":"2025-05-11T00:56:03Z","abstract_excerpt":"Automatic prompt optimization frameworks are developed to obtain suitable prompts for large language models (LLMs) with respect to desired output quality metrics. Although existing approaches can handle conventional tasks such as fixed-solution question answering, defining the metric becomes complicated when the output quality cannot be easily assessed by comparisons with standard golden samples. Consequently, optimizing the prompts effectively and efficiently without a clear metric becomes a critical challenge. To address the issue, we present PLHF (which stands for \"P\"rompt \"L\"earning with \""},"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":"2505.07886","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-05-11T00:56:03Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7e619ddaca1a2f526d977beebae9ea84d48f97f170b83eb7b5b97dd859267269","abstract_canon_sha256":"f62b3f6ac6876d20cc8b46f83142ef4aa764c0ebb9e513909c537e2b48bce6c8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:59.098401Z","signature_b64":"PiYG58PA7HDzzyy5h4nae0mjZLSuTPux1OztCSdUBF0dNm8d8tmZUKTslsNi/OQWnwAebEwPSNUxPKvkoEOnAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"809fabb51f4afbc5d573ee629453cb6032c888a6318b0a5d03cfc7b45e3c3ef6","last_reissued_at":"2026-07-05T11:01:59.097768Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:59.097768Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PLHF: Prompt Optimization with Few-Shot Human Feedback","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Chun-Pai Yang, Kan Zheng, Shou-De Lin","submitted_at":"2025-05-11T00:56:03Z","abstract_excerpt":"Automatic prompt optimization frameworks are developed to obtain suitable prompts for large language models (LLMs) with respect to desired output quality metrics. Although existing approaches can handle conventional tasks such as fixed-solution question answering, defining the metric becomes complicated when the output quality cannot be easily assessed by comparisons with standard golden samples. Consequently, optimizing the prompts effectively and efficiently without a clear metric becomes a critical challenge. To address the issue, we present PLHF (which stands for \"P\"rompt \"L\"earning with \""},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.07886","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/2505.07886/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":"2505.07886","created_at":"2026-07-05T11:01:59.097852+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.07886v1","created_at":"2026-07-05T11:01:59.097852+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.07886","created_at":"2026-07-05T11:01:59.097852+00:00"},{"alias_kind":"pith_short_12","alias_value":"QCP2XNI7JL54","created_at":"2026-07-05T11:01:59.097852+00:00"},{"alias_kind":"pith_short_16","alias_value":"QCP2XNI7JL54LVLT","created_at":"2026-07-05T11:01:59.097852+00:00"},{"alias_kind":"pith_short_8","alias_value":"QCP2XNI7","created_at":"2026-07-05T11:01:59.097852+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/QCP2XNI7JL54LVLT5ZRJIU6LMA","json":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA.json","graph_json":"https://pith.science/api/pith-number/QCP2XNI7JL54LVLT5ZRJIU6LMA/graph.json","events_json":"https://pith.science/api/pith-number/QCP2XNI7JL54LVLT5ZRJIU6LMA/events.json","paper":"https://pith.science/paper/QCP2XNI7"},"agent_actions":{"view_html":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA","download_json":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA.json","view_paper":"https://pith.science/paper/QCP2XNI7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.07886&json=true","fetch_graph":"https://pith.science/api/pith-number/QCP2XNI7JL54LVLT5ZRJIU6LMA/graph.json","fetch_events":"https://pith.science/api/pith-number/QCP2XNI7JL54LVLT5ZRJIU6LMA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA/action/storage_attestation","attest_author":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA/action/author_attestation","sign_citation":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA/action/citation_signature","submit_replication":"https://pith.science/pith/QCP2XNI7JL54LVLT5ZRJIU6LMA/action/replication_record"}},"created_at":"2026-07-05T11:01:59.097852+00:00","updated_at":"2026-07-05T11:01:59.097852+00:00"}