{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:X6W245LLDMBZZ2BYL3EPNC3FHL","short_pith_number":"pith:X6W245LL","schema_version":"1.0","canonical_sha256":"bfadae756b1b039ce8385ec8f68b653aea8b15987c1b26e82033be612edd65df","source":{"kind":"arxiv","id":"2505.19819","version":1},"attestation_state":"computed","paper":{"title":"FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CE","authors_text":"Dannong Wang, Daochen Zha, Jaisal Patel, Steve Y. Yang, Xiao-Yang Liu","submitted_at":"2025-05-26T10:58:51Z","abstract_excerpt":"Low-rank adaptation (LoRA) methods show great potential for scaling pre-trained general-purpose Large Language Models (LLMs) to hundreds or thousands of use scenarios. However, their efficacy in high-stakes domains like finance is rarely explored, e.g., passing CFA exams and analyzing SEC filings. In this paper, we present the open-source FinLoRA project that benchmarks LoRA methods on both general and highly professional financial tasks. First, we curated 19 datasets covering diverse financial applications; in particular, we created four novel XBRL analysis datasets based on 150 SEC filings. "},"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":"2505.19819","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CE","submitted_at":"2025-05-26T10:58:51Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"22f32301814573c78789761964ad47349593a088275782842bfd5cff93cac2b2","abstract_canon_sha256":"4bb12200d87486d9470ea06fad7c84290777fc673075407887f94c4b4c89285e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:09:48.347259Z","signature_b64":"Ob2szbBTBXPye3vzbK3l0DxnkSWrPC3ujYwggwggcl40JR1zjSNcLrksvdwz479jDJciYJ1VX9UbKpkIAUI+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfadae756b1b039ce8385ec8f68b653aea8b15987c1b26e82033be612edd65df","last_reissued_at":"2026-07-05T11:09:48.346731Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:09:48.346731Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FinLoRA: Benchmarking LoRA Methods for Fine-Tuning LLMs on Financial Datasets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CE","authors_text":"Dannong Wang, Daochen Zha, Jaisal Patel, Steve Y. Yang, Xiao-Yang Liu","submitted_at":"2025-05-26T10:58:51Z","abstract_excerpt":"Low-rank adaptation (LoRA) methods show great potential for scaling pre-trained general-purpose Large Language Models (LLMs) to hundreds or thousands of use scenarios. However, their efficacy in high-stakes domains like finance is rarely explored, e.g., passing CFA exams and analyzing SEC filings. In this paper, we present the open-source FinLoRA project that benchmarks LoRA methods on both general and highly professional financial tasks. First, we curated 19 datasets covering diverse financial applications; in particular, we created four novel XBRL analysis datasets based on 150 SEC filings. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.19819","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/2505.19819/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":"2505.19819","created_at":"2026-07-05T11:09:48.346793+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.19819v1","created_at":"2026-07-05T11:09:48.346793+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.19819","created_at":"2026-07-05T11:09:48.346793+00:00"},{"alias_kind":"pith_short_12","alias_value":"X6W245LLDMBZ","created_at":"2026-07-05T11:09:48.346793+00:00"},{"alias_kind":"pith_short_16","alias_value":"X6W245LLDMBZZ2BY","created_at":"2026-07-05T11:09:48.346793+00:00"},{"alias_kind":"pith_short_8","alias_value":"X6W245LL","created_at":"2026-07-05T11:09:48.346793+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.11182","citing_title":"EEVEE: Towards Test-time Prompt Learning in the Real World for Self-Improving Agents","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.31201","citing_title":"Point-in-Time Financial RAG with Frozen LLMs and Market-Feedback Adaptive Retrieval","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL","json":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL.json","graph_json":"https://pith.science/api/pith-number/X6W245LLDMBZZ2BYL3EPNC3FHL/graph.json","events_json":"https://pith.science/api/pith-number/X6W245LLDMBZZ2BYL3EPNC3FHL/events.json","paper":"https://pith.science/paper/X6W245LL"},"agent_actions":{"view_html":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL","download_json":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL.json","view_paper":"https://pith.science/paper/X6W245LL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.19819&json=true","fetch_graph":"https://pith.science/api/pith-number/X6W245LLDMBZZ2BYL3EPNC3FHL/graph.json","fetch_events":"https://pith.science/api/pith-number/X6W245LLDMBZZ2BYL3EPNC3FHL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL/action/storage_attestation","attest_author":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL/action/author_attestation","sign_citation":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL/action/citation_signature","submit_replication":"https://pith.science/pith/X6W245LLDMBZZ2BYL3EPNC3FHL/action/replication_record"}},"created_at":"2026-07-05T11:09:48.346793+00:00","updated_at":"2026-07-05T11:09:48.346793+00:00"}