{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:BGVEMCZBYDCAKWI2MWYN7PVONE","short_pith_number":"pith:BGVEMCZB","schema_version":"1.0","canonical_sha256":"09aa460b21c0c405591a65b0dfbeae690e2665a747fb1a8edff5919107fbc6c9","source":{"kind":"arxiv","id":"2303.08769","version":2},"attestation_state":"computed","paper":{"title":"Prompting Large Language Models With the Socratic Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward Y. Chang","submitted_at":"2023-02-17T23:25:57Z","abstract_excerpt":"This paper presents a systematic approach to using the Socratic method in developing prompt templates that effectively interact with large language models, including GPT-3. Various methods are examined, and those that yield precise answers and justifications while fostering creativity and imagination to enhance creative writing are identified. Techniques such as {\\em definition}, {\\em elenchus}, {\\em dialectic}, {\\em maieutics}, {\\em generalization}, and {\\em counterfactual reasoning} are discussed for their application in engineering prompt templates and their connections to inductive, deduct"},"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":"2303.08769","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-02-17T23:25:57Z","cross_cats_sorted":[],"title_canon_sha256":"643c5fe5cc37e4a826fa1dfa2bc4a6ab5af5c479e8867afd2d5524cada98c841","abstract_canon_sha256":"3914ebd40d631fdbc3e92288093296583fc9c31dd4e784af6d3263b07c0a4135"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:51:45.868013Z","signature_b64":"VDUuvGtlF5A0wMgEvBcktIj3E6lxA/JG1CdG+lC5U6YjXUYSHT9AYVLl/j9CYvuLkRliy220cRkSmbZnV7edCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"09aa460b21c0c405591a65b0dfbeae690e2665a747fb1a8edff5919107fbc6c9","last_reissued_at":"2026-07-05T05:51:45.867537Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:51:45.867537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prompting Large Language Models With the Socratic Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward Y. Chang","submitted_at":"2023-02-17T23:25:57Z","abstract_excerpt":"This paper presents a systematic approach to using the Socratic method in developing prompt templates that effectively interact with large language models, including GPT-3. Various methods are examined, and those that yield precise answers and justifications while fostering creativity and imagination to enhance creative writing are identified. Techniques such as {\\em definition}, {\\em elenchus}, {\\em dialectic}, {\\em maieutics}, {\\em generalization}, and {\\em counterfactual reasoning} are discussed for their application in engineering prompt templates and their connections to inductive, deduct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.08769","kind":"arxiv","version":2},"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/2303.08769/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":"2303.08769","created_at":"2026-07-05T05:51:45.867594+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.08769v2","created_at":"2026-07-05T05:51:45.867594+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.08769","created_at":"2026-07-05T05:51:45.867594+00:00"},{"alias_kind":"pith_short_12","alias_value":"BGVEMCZBYDCA","created_at":"2026-07-05T05:51:45.867594+00:00"},{"alias_kind":"pith_short_16","alias_value":"BGVEMCZBYDCAKWI2","created_at":"2026-07-05T05:51:45.867594+00:00"},{"alias_kind":"pith_short_8","alias_value":"BGVEMCZB","created_at":"2026-07-05T05:51:45.867594+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.16656","citing_title":"P-CoT: A Pedagogically-motivated Participatory Chain-of-Thought Prompting for Phonological Reasoning in LLMs","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE","json":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE.json","graph_json":"https://pith.science/api/pith-number/BGVEMCZBYDCAKWI2MWYN7PVONE/graph.json","events_json":"https://pith.science/api/pith-number/BGVEMCZBYDCAKWI2MWYN7PVONE/events.json","paper":"https://pith.science/paper/BGVEMCZB"},"agent_actions":{"view_html":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE","download_json":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE.json","view_paper":"https://pith.science/paper/BGVEMCZB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.08769&json=true","fetch_graph":"https://pith.science/api/pith-number/BGVEMCZBYDCAKWI2MWYN7PVONE/graph.json","fetch_events":"https://pith.science/api/pith-number/BGVEMCZBYDCAKWI2MWYN7PVONE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE/action/storage_attestation","attest_author":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE/action/author_attestation","sign_citation":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE/action/citation_signature","submit_replication":"https://pith.science/pith/BGVEMCZBYDCAKWI2MWYN7PVONE/action/replication_record"}},"created_at":"2026-07-05T05:51:45.867594+00:00","updated_at":"2026-07-05T05:51:45.867594+00:00"}