{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RGL6UGNQQD5NSAR6CKV5VQHIL3","short_pith_number":"pith:RGL6UGNQ","schema_version":"1.0","canonical_sha256":"8997ea19b080fad9023e12abdac0e85ecd5e0b21528556c36b5d269384d5069e","source":{"kind":"arxiv","id":"2410.10542","version":1},"attestation_state":"computed","paper":{"title":"Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aniket Deroy, Arnab Bhattacharya, Shubham Kumar Nigam, Subhankar Maity","submitted_at":"2024-10-14T14:22:12Z","abstract_excerpt":"This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BERT, and XLNet, alongside LLMs such as Llama-2 and GPT-3.5 Turbo. In this realistic scenario, we simulate how judgments are predicted at the point when a case is presented for a decision in court, using only the information available at that time, such as the facts of the case, statutes, precedents, and arguments. This approach mimics real-world conditions, where decisions must be made without the benefit of hindsight"},"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":"2410.10542","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-14T14:22:12Z","cross_cats_sorted":["cs.AI","cs.IR","cs.LG"],"title_canon_sha256":"c87003a09fe2d92a681a72fc0e518f602dabd900b9e909df811e0e51151cde14","abstract_canon_sha256":"37e87d26158ff6cce66a150f11f732e81e1d18ec0db7e5e2d7aa05796c2b2509"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:17.643548Z","signature_b64":"7qFpKqKc7Qhg1AfSnrF/mKgrGYoNlmgfNtBt4MdN2GTWOk9Iple53bj6mgsSXp7FV4NTI8bKoO8HmzYSeAJoBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8997ea19b080fad9023e12abdac0e85ecd5e0b21528556c36b5d269384d5069e","last_reissued_at":"2026-07-05T09:20:17.643058Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:17.643058Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rethinking Legal Judgement Prediction in a Realistic Scenario in the Era of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.IR","cs.LG"],"primary_cat":"cs.CL","authors_text":"Aniket Deroy, Arnab Bhattacharya, Shubham Kumar Nigam, Subhankar Maity","submitted_at":"2024-10-14T14:22:12Z","abstract_excerpt":"This study investigates judgment prediction in a realistic scenario within the context of Indian judgments, utilizing a range of transformer-based models, including InLegalBERT, BERT, and XLNet, alongside LLMs such as Llama-2 and GPT-3.5 Turbo. In this realistic scenario, we simulate how judgments are predicted at the point when a case is presented for a decision in court, using only the information available at that time, such as the facts of the case, statutes, precedents, and arguments. This approach mimics real-world conditions, where decisions must be made without the benefit of hindsight"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.10542","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/2410.10542/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":"2410.10542","created_at":"2026-07-05T09:20:17.643118+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.10542v1","created_at":"2026-07-05T09:20:17.643118+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.10542","created_at":"2026-07-05T09:20:17.643118+00:00"},{"alias_kind":"pith_short_12","alias_value":"RGL6UGNQQD5N","created_at":"2026-07-05T09:20:17.643118+00:00"},{"alias_kind":"pith_short_16","alias_value":"RGL6UGNQQD5NSAR6","created_at":"2026-07-05T09:20:17.643118+00:00"},{"alias_kind":"pith_short_8","alias_value":"RGL6UGNQ","created_at":"2026-07-05T09:20:17.643118+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.02011","citing_title":"Enhancing Judgment Document Generation via Agentic Legal Information Collection and Rubric-Guided Optimization","ref_index":18,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3","json":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3.json","graph_json":"https://pith.science/api/pith-number/RGL6UGNQQD5NSAR6CKV5VQHIL3/graph.json","events_json":"https://pith.science/api/pith-number/RGL6UGNQQD5NSAR6CKV5VQHIL3/events.json","paper":"https://pith.science/paper/RGL6UGNQ"},"agent_actions":{"view_html":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3","download_json":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3.json","view_paper":"https://pith.science/paper/RGL6UGNQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.10542&json=true","fetch_graph":"https://pith.science/api/pith-number/RGL6UGNQQD5NSAR6CKV5VQHIL3/graph.json","fetch_events":"https://pith.science/api/pith-number/RGL6UGNQQD5NSAR6CKV5VQHIL3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3/action/storage_attestation","attest_author":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3/action/author_attestation","sign_citation":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3/action/citation_signature","submit_replication":"https://pith.science/pith/RGL6UGNQQD5NSAR6CKV5VQHIL3/action/replication_record"}},"created_at":"2026-07-05T09:20:17.643118+00:00","updated_at":"2026-07-05T09:20:17.643118+00:00"}