{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YT3JA7H3ORDOBALU3NTOQKQVYX","short_pith_number":"pith:YT3JA7H3","schema_version":"1.0","canonical_sha256":"c4f6907cfb7446e08174db66e82a15c5ffa268cfa45d38b5abb5aa6e884f387a","source":{"kind":"arxiv","id":"2412.14771","version":1},"attestation_state":"computed","paper":{"title":"ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Banan Tantour, Mohannad Hendi, Rabee Qasem","submitted_at":"2024-12-19T11:55:51Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable potential in diverse domains, yet their application in the legal sector, particularly in low-resource contexts, remains limited. This study addresses the challenges of adapting LLMs to the Palestinian legal domain, where political instability, fragmented legal frameworks, and limited AI resources hinder effective machine-learning applications. We present a fine-tuned model based on a quantized version of Llama-3.2-1B-Instruct, trained on a synthetic data set derived from Palestinian legal texts. Using smaller-scale models and strategica"},"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":"2412.14771","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-19T11:55:51Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"60cd9f17befe9b707187b93c81bf4228fa48b35371441e6681f0b93a30dcdc67","abstract_canon_sha256":"a3db4b22cd342a65877ff4b72235d8a38be471770858e955d22a074b09bacd5d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:52.516478Z","signature_b64":"yYx3SJE3wsrmUU3dVGBIRbGdYu4veIf0VjJvdgpJ45kCQHbfktVq1CWxCQhUBqT5rk2iEOG21LY3GnJlql8jDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4f6907cfb7446e08174db66e82a15c5ffa268cfa45d38b5abb5aa6e884f387a","last_reissued_at":"2026-07-05T09:51:52.515985Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:52.515985Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ALKAFI-LLAMA3: Fine-Tuning LLMs for Precise Legal Understanding in Palestine","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Banan Tantour, Mohannad Hendi, Rabee Qasem","submitted_at":"2024-12-19T11:55:51Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable potential in diverse domains, yet their application in the legal sector, particularly in low-resource contexts, remains limited. This study addresses the challenges of adapting LLMs to the Palestinian legal domain, where political instability, fragmented legal frameworks, and limited AI resources hinder effective machine-learning applications. We present a fine-tuned model based on a quantized version of Llama-3.2-1B-Instruct, trained on a synthetic data set derived from Palestinian legal texts. Using smaller-scale models and strategica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.14771","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/2412.14771/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":"2412.14771","created_at":"2026-07-05T09:51:52.516044+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.14771v1","created_at":"2026-07-05T09:51:52.516044+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.14771","created_at":"2026-07-05T09:51:52.516044+00:00"},{"alias_kind":"pith_short_12","alias_value":"YT3JA7H3ORDO","created_at":"2026-07-05T09:51:52.516044+00:00"},{"alias_kind":"pith_short_16","alias_value":"YT3JA7H3ORDOBALU","created_at":"2026-07-05T09:51:52.516044+00:00"},{"alias_kind":"pith_short_8","alias_value":"YT3JA7H3","created_at":"2026-07-05T09:51:52.516044+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/YT3JA7H3ORDOBALU3NTOQKQVYX","json":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX.json","graph_json":"https://pith.science/api/pith-number/YT3JA7H3ORDOBALU3NTOQKQVYX/graph.json","events_json":"https://pith.science/api/pith-number/YT3JA7H3ORDOBALU3NTOQKQVYX/events.json","paper":"https://pith.science/paper/YT3JA7H3"},"agent_actions":{"view_html":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX","download_json":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX.json","view_paper":"https://pith.science/paper/YT3JA7H3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.14771&json=true","fetch_graph":"https://pith.science/api/pith-number/YT3JA7H3ORDOBALU3NTOQKQVYX/graph.json","fetch_events":"https://pith.science/api/pith-number/YT3JA7H3ORDOBALU3NTOQKQVYX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX/action/storage_attestation","attest_author":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX/action/author_attestation","sign_citation":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX/action/citation_signature","submit_replication":"https://pith.science/pith/YT3JA7H3ORDOBALU3NTOQKQVYX/action/replication_record"}},"created_at":"2026-07-05T09:51:52.516044+00:00","updated_at":"2026-07-05T09:51:52.516044+00:00"}