{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:QZCXYXCDGLYZVHX2DILRDQUGFI","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":"6f657f3c4bdc65f7cbb2574fdc05a2b1c1802b90f900d0645bf1c382933c090b","cross_cats_sorted":["cs.AI","cs.ET","cs.HC","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-27T17:25:16Z","title_canon_sha256":"7b754681072bd192065fd5aca24179e7b660f11a653c3aee696b4f5f36ed475b"},"schema_version":"1.0","source":{"id":"2409.00112","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2409.00112","created_at":"2026-07-05T09:01:41Z"},{"alias_kind":"arxiv_version","alias_value":"2409.00112v1","created_at":"2026-07-05T09:01:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.00112","created_at":"2026-07-05T09:01:41Z"},{"alias_kind":"pith_short_12","alias_value":"QZCXYXCDGLYZ","created_at":"2026-07-05T09:01:41Z"},{"alias_kind":"pith_short_16","alias_value":"QZCXYXCDGLYZVHX2","created_at":"2026-07-05T09:01:41Z"},{"alias_kind":"pith_short_8","alias_value":"QZCXYXCD","created_at":"2026-07-05T09:01:41Z"}],"graph_snapshots":[{"event_id":"sha256:84748ddbd51282b4f65dae57155d95d4538eb29e6f7e0e9a1ee0bb5d1a8b0ff6","target":"graph","created_at":"2026-07-05T09:01:41Z","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/2409.00112/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While Large Language Models (LLMs) are being quickly adapted to many domains, including healthcare, their strengths and pitfalls remain under-explored. In our study, we examine the effects of prompt engineering to guide Large Language Models (LLMs) in delivering parts of a Problem-Solving Therapy (PST) session via text, particularly during the symptom identification and assessment phase for personalized goal setting. We present evaluation results of the models' performances by automatic metrics and experienced medical professionals. We demonstrate that the models' capability to deliver protoco","authors_text":"Caroline El Jazmi, Daniil Filienko, Martine De Cock, Serena Xie, Trevor Cohen, Weichao Yuwen, Yinzhou Wang","cross_cats":["cs.AI","cs.ET","cs.HC","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-27T17:25:16Z","title":"Toward Large Language Models as a Therapeutic Tool: Comparing Prompting Techniques to Improve GPT-Delivered Problem-Solving Therapy"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.00112","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":"sha256:78aa4593c46c6fa35351fe0afd7061b1e96bf58a2159cd0159c99836a8477c50","target":"record","created_at":"2026-07-05T09:01:41Z","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":"6f657f3c4bdc65f7cbb2574fdc05a2b1c1802b90f900d0645bf1c382933c090b","cross_cats_sorted":["cs.AI","cs.ET","cs.HC","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-27T17:25:16Z","title_canon_sha256":"7b754681072bd192065fd5aca24179e7b660f11a653c3aee696b4f5f36ed475b"},"schema_version":"1.0","source":{"id":"2409.00112","kind":"arxiv","version":1}},"canonical_sha256":"86457c5c4332f19a9efa1a1711c2862a0fbc94803a9ba6d9355e0b6fa58342bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"86457c5c4332f19a9efa1a1711c2862a0fbc94803a9ba6d9355e0b6fa58342bd","first_computed_at":"2026-07-05T09:01:41.040150Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:01:41.040150Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HwOrU/kRgbOoYkFE//IecGQDKNUWiaJ0qErjpr6ZIQShaf4uNr+zQ2q3dwqR9VbHxZeIEbH/OSl94OJB+PlfDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T09:01:41.040624Z","signed_message":"canonical_sha256_bytes"},"source_id":"2409.00112","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:78aa4593c46c6fa35351fe0afd7061b1e96bf58a2159cd0159c99836a8477c50","sha256:84748ddbd51282b4f65dae57155d95d4538eb29e6f7e0e9a1ee0bb5d1a8b0ff6"],"state_sha256":"0b88c2bbee1c10cad05d4cbdc85690606666b43d98a0794765f983a8de67975c"}