{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5JUQ6AHP6OSLEWR5KDIGXU7USZ","short_pith_number":"pith:5JUQ6AHP","schema_version":"1.0","canonical_sha256":"ea690f00eff3a4b25a3d50d06bd3f49654758b3d1e768bbe787115b71f79f2f8","source":{"kind":"arxiv","id":"2306.12659","version":1},"attestation_state":"computed","paper":{"title":"Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.ST","q-fin.TR"],"primary_cat":"cs.CL","authors_text":"Boyu Zhang, Hongyang Yang, Xiao-Yang Liu","submitted_at":"2023-06-22T03:56:38Z","abstract_excerpt":"Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentime"},"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":"2306.12659","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-06-22T03:56:38Z","cross_cats_sorted":["cs.LG","q-fin.ST","q-fin.TR"],"title_canon_sha256":"092c6b27db41ffefcacea39c973dc9876f6b4030f38b53144952fd19223ceadc","abstract_canon_sha256":"59ebf1308b780f5c5ebdae4aa2cbe91314ab298f4af5adc099f501cb80c18722"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:23:41.127565Z","signature_b64":"uu2NzVJ+OURM58tK9qrDyynVBdzqHcY7rWmyJR1XAVSeL0F1kzf5JswEihZcLwoov6KBYPrrHJHbwDI6MVdACw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea690f00eff3a4b25a3d50d06bd3f49654758b3d1e768bbe787115b71f79f2f8","last_reissued_at":"2026-07-05T06:23:41.127090Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:23:41.127090Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-fin.ST","q-fin.TR"],"primary_cat":"cs.CL","authors_text":"Boyu Zhang, Hongyang Yang, Xiao-Yang Liu","submitted_at":"2023-06-22T03:56:38Z","abstract_excerpt":"Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentime"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.12659","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/2306.12659/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":"2306.12659","created_at":"2026-07-05T06:23:41.127141+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.12659v1","created_at":"2026-07-05T06:23:41.127141+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.12659","created_at":"2026-07-05T06:23:41.127141+00:00"},{"alias_kind":"pith_short_12","alias_value":"5JUQ6AHP6OSL","created_at":"2026-07-05T06:23:41.127141+00:00"},{"alias_kind":"pith_short_16","alias_value":"5JUQ6AHP6OSLEWR5","created_at":"2026-07-05T06:23:41.127141+00:00"},{"alias_kind":"pith_short_8","alias_value":"5JUQ6AHP","created_at":"2026-07-05T06:23:41.127141+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.23007","citing_title":"MadEvolve: Evolutionary Optimization of Trading Systems with Large Language Models","ref_index":15,"is_internal_anchor":false},{"citing_arxiv_id":"2410.14927","citing_title":"Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2503.22693","citing_title":"Bridging Language Models and Financial Analysis","ref_index":118,"is_internal_anchor":false},{"citing_arxiv_id":"2506.11512","citing_title":"From Time Series Analysis to Question Answering: A Survey in the LLM Era","ref_index":121,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09106","citing_title":"Fin-Bias: Comprehensive Evaluation for LLM Decision-Making under human bias in Finance Domain","ref_index":77,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ","json":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ.json","graph_json":"https://pith.science/api/pith-number/5JUQ6AHP6OSLEWR5KDIGXU7USZ/graph.json","events_json":"https://pith.science/api/pith-number/5JUQ6AHP6OSLEWR5KDIGXU7USZ/events.json","paper":"https://pith.science/paper/5JUQ6AHP"},"agent_actions":{"view_html":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ","download_json":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ.json","view_paper":"https://pith.science/paper/5JUQ6AHP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.12659&json=true","fetch_graph":"https://pith.science/api/pith-number/5JUQ6AHP6OSLEWR5KDIGXU7USZ/graph.json","fetch_events":"https://pith.science/api/pith-number/5JUQ6AHP6OSLEWR5KDIGXU7USZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ/action/storage_attestation","attest_author":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ/action/author_attestation","sign_citation":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ/action/citation_signature","submit_replication":"https://pith.science/pith/5JUQ6AHP6OSLEWR5KDIGXU7USZ/action/replication_record"}},"created_at":"2026-07-05T06:23:41.127141+00:00","updated_at":"2026-07-05T06:23:41.127141+00:00"}