{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZFEN6WOT32XWXWNW5OUVRV5HQN","short_pith_number":"pith:ZFEN6WOT","schema_version":"1.0","canonical_sha256":"c948df59d3deaf6bd9b6eba958d7a7836c13b0c2e6e217104e332802937970e3","source":{"kind":"arxiv","id":"2401.14043","version":3},"attestation_state":"computed","paper":{"title":"Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Haochen Li, Jonathan Leung, Zhiqi Shen","submitted_at":"2024-01-25T09:47:55Z","abstract_excerpt":"Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 representative studies, we demonstrate that a goal-oriented prompt formulation, which guides LLMs to follow established human logical thinking, significantly improves the performance of LLMs. Fu"},"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":"2401.14043","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-01-25T09:47:55Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"236c62dede7eda2d4a1be440bf379de38578e2490f7512f56212042d8c63a461","abstract_canon_sha256":"c43fb72b07fdfad8b235b5747d8be1ed8f5ee7d8a5a9a4f557731329099166aa"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:07:45.722607Z","signature_b64":"xr+nnDdhBt2Y31QHGXdEYpI7ARpjoIerlFECeX/8QwA3wpEoTIu2HjJx1tyxucyjethi85RllTvBItNLw2TcAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c948df59d3deaf6bd9b6eba958d7a7836c13b0c2e6e217104e332802937970e3","last_reissued_at":"2026-07-05T09:07:45.722062Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:07:45.722062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Towards Goal-oriented Prompt Engineering for Large Language Models: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Haochen Li, Jonathan Leung, Zhiqi Shen","submitted_at":"2024-01-25T09:47:55Z","abstract_excerpt":"Large Language Models (LLMs) have shown prominent performance in various downstream tasks and prompt engineering plays a pivotal role in optimizing LLMs' performance. This paper, not only as an overview of current prompt engineering methods, but also aims to highlight the limitation of designing prompts based on an anthropomorphic assumption that expects LLMs to think like humans. From our review of 50 representative studies, we demonstrate that a goal-oriented prompt formulation, which guides LLMs to follow established human logical thinking, significantly improves the performance of LLMs. Fu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.14043","kind":"arxiv","version":3},"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/2401.14043/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":"2401.14043","created_at":"2026-07-05T09:07:45.722121+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.14043v3","created_at":"2026-07-05T09:07:45.722121+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.14043","created_at":"2026-07-05T09:07:45.722121+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZFEN6WOT32XW","created_at":"2026-07-05T09:07:45.722121+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZFEN6WOT32XWXWNW","created_at":"2026-07-05T09:07:45.722121+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZFEN6WOT","created_at":"2026-07-05T09:07:45.722121+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.17356","citing_title":"Automatic Large Language Models Creation of Interactive Learning Lessons","ref_index":13,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN","json":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN.json","graph_json":"https://pith.science/api/pith-number/ZFEN6WOT32XWXWNW5OUVRV5HQN/graph.json","events_json":"https://pith.science/api/pith-number/ZFEN6WOT32XWXWNW5OUVRV5HQN/events.json","paper":"https://pith.science/paper/ZFEN6WOT"},"agent_actions":{"view_html":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN","download_json":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN.json","view_paper":"https://pith.science/paper/ZFEN6WOT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.14043&json=true","fetch_graph":"https://pith.science/api/pith-number/ZFEN6WOT32XWXWNW5OUVRV5HQN/graph.json","fetch_events":"https://pith.science/api/pith-number/ZFEN6WOT32XWXWNW5OUVRV5HQN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN/action/storage_attestation","attest_author":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN/action/author_attestation","sign_citation":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN/action/citation_signature","submit_replication":"https://pith.science/pith/ZFEN6WOT32XWXWNW5OUVRV5HQN/action/replication_record"}},"created_at":"2026-07-05T09:07:45.722121+00:00","updated_at":"2026-07-05T09:07:45.722121+00:00"}