{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:SX4B2YIWTA2PUWHVQW73ZX2JZM","short_pith_number":"pith:SX4B2YIW","schema_version":"1.0","canonical_sha256":"95f81d61169834fa58f585bfbcdf49cb3224308ea896c49d24fa625b417b76d7","source":{"kind":"arxiv","id":"2311.15490","version":1},"attestation_state":"computed","paper":{"title":"Optimizing and Fine-tuning Large Language Model for Urban Renewal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Liang Zhang, Peng Gong, Shaolan Wang, Tom Zhang, Xianyao Ling, Xi Wang, Xuecao Li, Zhixing Li","submitted_at":"2023-11-27T02:17:11Z","abstract_excerpt":"This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks. Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal. By guiding the LLM to automatically generate QA data based on prompt words and given text, it is poss"},"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":"2311.15490","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-11-27T02:17:11Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"87e749799eb9a740dcaa83cd9a21eb375d76de5b9b519a7d0bbd9c553e8f7d22","abstract_canon_sha256":"12cf30b75a8c62eac0fda395bff4769aa70e8b81f4bab400ca34d415eaa994bd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:17:04.702554Z","signature_b64":"aGLvnLmxIfUApzS5PfCQmNLSFEMvINDkHL8BQWsLdB0bpuOl+ooWx6YojX9WJm9Nirzf/N6lbqMYIZ7EdANDCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"95f81d61169834fa58f585bfbcdf49cb3224308ea896c49d24fa625b417b76d7","last_reissued_at":"2026-07-05T07:17:04.702059Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:17:04.702059Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing and Fine-tuning Large Language Model for Urban Renewal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Liang Zhang, Peng Gong, Shaolan Wang, Tom Zhang, Xianyao Ling, Xi Wang, Xuecao Li, Zhixing Li","submitted_at":"2023-11-27T02:17:11Z","abstract_excerpt":"This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks. Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal. By guiding the LLM to automatically generate QA data based on prompt words and given text, it is poss"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.15490","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/2311.15490/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":"2311.15490","created_at":"2026-07-05T07:17:04.702117+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.15490v1","created_at":"2026-07-05T07:17:04.702117+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.15490","created_at":"2026-07-05T07:17:04.702117+00:00"},{"alias_kind":"pith_short_12","alias_value":"SX4B2YIWTA2P","created_at":"2026-07-05T07:17:04.702117+00:00"},{"alias_kind":"pith_short_16","alias_value":"SX4B2YIWTA2PUWHV","created_at":"2026-07-05T07:17:04.702117+00:00"},{"alias_kind":"pith_short_8","alias_value":"SX4B2YIW","created_at":"2026-07-05T07:17:04.702117+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/SX4B2YIWTA2PUWHVQW73ZX2JZM","json":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM.json","graph_json":"https://pith.science/api/pith-number/SX4B2YIWTA2PUWHVQW73ZX2JZM/graph.json","events_json":"https://pith.science/api/pith-number/SX4B2YIWTA2PUWHVQW73ZX2JZM/events.json","paper":"https://pith.science/paper/SX4B2YIW"},"agent_actions":{"view_html":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM","download_json":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM.json","view_paper":"https://pith.science/paper/SX4B2YIW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.15490&json=true","fetch_graph":"https://pith.science/api/pith-number/SX4B2YIWTA2PUWHVQW73ZX2JZM/graph.json","fetch_events":"https://pith.science/api/pith-number/SX4B2YIWTA2PUWHVQW73ZX2JZM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM/action/storage_attestation","attest_author":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM/action/author_attestation","sign_citation":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM/action/citation_signature","submit_replication":"https://pith.science/pith/SX4B2YIWTA2PUWHVQW73ZX2JZM/action/replication_record"}},"created_at":"2026-07-05T07:17:04.702117+00:00","updated_at":"2026-07-05T07:17:04.702117+00:00"}