{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MLJCGQDXYFUDTK575UT5JR4BFA","short_pith_number":"pith:MLJCGQDX","canonical_record":{"source":{"id":"2310.04027","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-06T05:40:23Z","cross_cats_sorted":["q-fin.ST","q-fin.TR"],"title_canon_sha256":"8affb8324a44e6a3357e17994a12bbc2798d77585d6ec991a12f6f12d1b93c3c","abstract_canon_sha256":"b82826113bdec8ade50c7cd30425b6d1c4a6605798193f9bfe422a09d57bb5a4"},"schema_version":"1.0"},"canonical_sha256":"62d2234077c16839abbfed27d4c78128296e4c8ccf9bde65ad5c1e19df6d9d0e","source":{"kind":"arxiv","id":"2310.04027","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04027","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04027v2","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04027","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_12","alias_value":"MLJCGQDXYFUD","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_16","alias_value":"MLJCGQDXYFUDTK57","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_8","alias_value":"MLJCGQDX","created_at":"2026-07-05T07:09:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MLJCGQDXYFUDTK575UT5JR4BFA","target":"record","payload":{"canonical_record":{"source":{"id":"2310.04027","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-06T05:40:23Z","cross_cats_sorted":["q-fin.ST","q-fin.TR"],"title_canon_sha256":"8affb8324a44e6a3357e17994a12bbc2798d77585d6ec991a12f6f12d1b93c3c","abstract_canon_sha256":"b82826113bdec8ade50c7cd30425b6d1c4a6605798193f9bfe422a09d57bb5a4"},"schema_version":"1.0"},"canonical_sha256":"62d2234077c16839abbfed27d4c78128296e4c8ccf9bde65ad5c1e19df6d9d0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:09:02.134792Z","signature_b64":"YCKZqgXjOCg+If7lNMXf8kyUfRDDh9INflXr3/lEdXYOBgO898fybWdilKGBBgRxKhSn918PGEfCD7YmCrF1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62d2234077c16839abbfed27d4c78128296e4c8ccf9bde65ad5c1e19df6d9d0e","last_reissued_at":"2026-07-05T07:09:02.134350Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:09:02.134350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.04027","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:09:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5onPwQ3QX8V6Coc9kSx6mUdlWnSMt9Z9E90fFUln8L6DDfMSI2evKsFAsvZ420bzVYBr/c6d0NqUKuYWragOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T10:45:34.861796Z"},"content_sha256":"417dc3eceb6d0dfe83c1df095ba38444d979e117249aa3987651dd1e3d122a9e","schema_version":"1.0","event_id":"sha256:417dc3eceb6d0dfe83c1df095ba38444d979e117249aa3987651dd1e3d122a9e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MLJCGQDXYFUDTK575UT5JR4BFA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.ST","q-fin.TR"],"primary_cat":"cs.CL","authors_text":"Ali Babar, Boyu Zhang, Hongyang Yang, Tianyu Zhou, Xiao-Yang Liu","submitted_at":"2023-10-06T05:40:23Z","abstract_excerpt":"Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks due to their commendable zero-shot abilities. Yet, directly applying LLMs to financial sentiment analysis presents challenges: The discrepancy between the pre-training objective of LLMs"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04027","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/2310.04027/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:09:02Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BBSSp10RkjOr3pMAYu0c0FofS/cwj6HpAA9/jDZ0WfN6dqZuFrcVxHR8nkOWm2aP7T1ZcoPMWJApQRT2asvOBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-10T10:45:34.862170Z"},"content_sha256":"669aba36de8d02fdd717497ba6234039d31e4008afc8cfdfd2f110a826e11fe2","schema_version":"1.0","event_id":"sha256:669aba36de8d02fdd717497ba6234039d31e4008afc8cfdfd2f110a826e11fe2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MLJCGQDXYFUDTK575UT5JR4BFA/bundle.json","state_url":"https://pith.science/pith/MLJCGQDXYFUDTK575UT5JR4BFA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MLJCGQDXYFUDTK575UT5JR4BFA/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-10T10:45:34Z","links":{"resolver":"https://pith.science/pith/MLJCGQDXYFUDTK575UT5JR4BFA","bundle":"https://pith.science/pith/MLJCGQDXYFUDTK575UT5JR4BFA/bundle.json","state":"https://pith.science/pith/MLJCGQDXYFUDTK575UT5JR4BFA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MLJCGQDXYFUDTK575UT5JR4BFA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MLJCGQDXYFUDTK575UT5JR4BFA","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b82826113bdec8ade50c7cd30425b6d1c4a6605798193f9bfe422a09d57bb5a4","cross_cats_sorted":["q-fin.ST","q-fin.TR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-06T05:40:23Z","title_canon_sha256":"8affb8324a44e6a3357e17994a12bbc2798d77585d6ec991a12f6f12d1b93c3c"},"schema_version":"1.0","source":{"id":"2310.04027","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.04027","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"arxiv_version","alias_value":"2310.04027v2","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.04027","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_12","alias_value":"MLJCGQDXYFUD","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_16","alias_value":"MLJCGQDXYFUDTK57","created_at":"2026-07-05T07:09:02Z"},{"alias_kind":"pith_short_8","alias_value":"MLJCGQDX","created_at":"2026-07-05T07:09:02Z"}],"graph_snapshots":[{"event_id":"sha256:669aba36de8d02fdd717497ba6234039d31e4008afc8cfdfd2f110a826e11fe2","target":"graph","created_at":"2026-07-05T07:09:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2310.04027/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Financial sentiment analysis is critical for valuation and investment decision-making. Traditional NLP models, however, are limited by their parameter size and the scope of their training datasets, which hampers their generalization capabilities and effectiveness in this field. Recently, Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks due to their commendable zero-shot abilities. Yet, directly applying LLMs to financial sentiment analysis presents challenges: The discrepancy between the pre-training objective of LLMs","authors_text":"Ali Babar, Boyu Zhang, Hongyang Yang, Tianyu Zhou, Xiao-Yang Liu","cross_cats":["q-fin.ST","q-fin.TR"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-06T05:40:23Z","title":"Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.04027","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:417dc3eceb6d0dfe83c1df095ba38444d979e117249aa3987651dd1e3d122a9e","target":"record","created_at":"2026-07-05T07:09:02Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"b82826113bdec8ade50c7cd30425b6d1c4a6605798193f9bfe422a09d57bb5a4","cross_cats_sorted":["q-fin.ST","q-fin.TR"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-10-06T05:40:23Z","title_canon_sha256":"8affb8324a44e6a3357e17994a12bbc2798d77585d6ec991a12f6f12d1b93c3c"},"schema_version":"1.0","source":{"id":"2310.04027","kind":"arxiv","version":2}},"canonical_sha256":"62d2234077c16839abbfed27d4c78128296e4c8ccf9bde65ad5c1e19df6d9d0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62d2234077c16839abbfed27d4c78128296e4c8ccf9bde65ad5c1e19df6d9d0e","first_computed_at":"2026-07-05T07:09:02.134350Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:09:02.134350Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"YCKZqgXjOCg+If7lNMXf8kyUfRDDh9INflXr3/lEdXYOBgO898fybWdilKGBBgRxKhSn918PGEfCD7YmCrF1Ag==","signature_status":"signed_v1","signed_at":"2026-07-05T07:09:02.134792Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.04027","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:417dc3eceb6d0dfe83c1df095ba38444d979e117249aa3987651dd1e3d122a9e","sha256:669aba36de8d02fdd717497ba6234039d31e4008afc8cfdfd2f110a826e11fe2"],"state_sha256":"f916873bb577b8ca86585df1abdd1015e77dcb6cbb95a6d2ba46e6d947212991"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VX9kuH7d6fRebRuyzQf1dU6zNUFbogA1TP17gxj4QRjQFhiGU5iLNtHbQyZSOXRjWVGthGWRKege+ANIg5fFCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-10T10:45:34.864052Z","bundle_sha256":"a21b4306ad84f65097af2e6f7e39bac1bdc1dcc98e4590bef6301bf947e96fc2"}}