{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:NZF3GSJJ7JJUVUJHUO222ONXQ5","short_pith_number":"pith:NZF3GSJJ","schema_version":"1.0","canonical_sha256":"6e4bb34929fa534ad127a3b5ad39b78751cd074c4d6079248c992a789911037e","source":{"kind":"arxiv","id":"2501.09768","version":3},"attestation_state":"computed","paper":{"title":"Can Large Language Models Predict the Outcome of Judicial Decisions?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali Ezzat Shahroor, Amani Al-Ghraibah, Mohamed Bayan Kmainasi","submitted_at":"2025-01-15T11:32:35Z","abstract_excerpt":"Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.2-3B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using LoRA. Additionally"},"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":"2501.09768","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-01-15T11:32:35Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"666682802342334773959647a8a6eb67f6a1b770e541ce897de16ce528c065c8","abstract_canon_sha256":"d48725d6f523a9d547c2aee0130715e6a8c83a5f919277aa8a0881a5ea6d74de"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:21:52.216598Z","signature_b64":"3d64DZZkptTZ9b4TC22UuVPVlMGEWtsu0XsGqBVYDgfm55leRk3Z+N9anox6d8fGKsso2FmRvCSXtF1zef0DBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6e4bb34929fa534ad127a3b5ad39b78751cd074c4d6079248c992a789911037e","last_reissued_at":"2026-07-05T10:21:52.216114Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:21:52.216114Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can Large Language Models Predict the Outcome of Judicial Decisions?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali Ezzat Shahroor, Amani Al-Ghraibah, Mohamed Bayan Kmainasi","submitted_at":"2025-01-15T11:32:35Z","abstract_excerpt":"Large Language Models (LLMs) have shown exceptional capabilities in Natural Language Processing (NLP) across diverse domains. However, their application in specialized tasks such as Legal Judgment Prediction (LJP) for low-resource languages like Arabic remains underexplored. In this work, we address this gap by developing an Arabic LJP dataset, collected and preprocessed from Saudi commercial court judgments. We benchmark state-of-the-art open-source LLMs, including LLaMA-3.2-3B and LLaMA-3.1-8B, under varying configurations such as zero-shot, one-shot, and fine-tuning using LoRA. Additionally"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.09768","kind":"arxiv","version":3},"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/2501.09768/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":"2501.09768","created_at":"2026-07-05T10:21:52.216172+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.09768v3","created_at":"2026-07-05T10:21:52.216172+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.09768","created_at":"2026-07-05T10:21:52.216172+00:00"},{"alias_kind":"pith_short_12","alias_value":"NZF3GSJJ7JJU","created_at":"2026-07-05T10:21:52.216172+00:00"},{"alias_kind":"pith_short_16","alias_value":"NZF3GSJJ7JJUVUJH","created_at":"2026-07-05T10:21:52.216172+00:00"},{"alias_kind":"pith_short_8","alias_value":"NZF3GSJJ","created_at":"2026-07-05T10:21:52.216172+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.17674","citing_title":"Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5","json":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5.json","graph_json":"https://pith.science/api/pith-number/NZF3GSJJ7JJUVUJHUO222ONXQ5/graph.json","events_json":"https://pith.science/api/pith-number/NZF3GSJJ7JJUVUJHUO222ONXQ5/events.json","paper":"https://pith.science/paper/NZF3GSJJ"},"agent_actions":{"view_html":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5","download_json":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5.json","view_paper":"https://pith.science/paper/NZF3GSJJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.09768&json=true","fetch_graph":"https://pith.science/api/pith-number/NZF3GSJJ7JJUVUJHUO222ONXQ5/graph.json","fetch_events":"https://pith.science/api/pith-number/NZF3GSJJ7JJUVUJHUO222ONXQ5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5/action/storage_attestation","attest_author":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5/action/author_attestation","sign_citation":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5/action/citation_signature","submit_replication":"https://pith.science/pith/NZF3GSJJ7JJUVUJHUO222ONXQ5/action/replication_record"}},"created_at":"2026-07-05T10:21:52.216172+00:00","updated_at":"2026-07-05T10:21:52.216172+00:00"}