{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:IYGP75IMJDVGHJMEUOARGGQLTU","short_pith_number":"pith:IYGP75IM","schema_version":"1.0","canonical_sha256":"460cfff50c48ea63a584a381131a0b9d1b83a7538e8ee6d75d2859a7c0ed1f09","source":{"kind":"arxiv","id":"2606.11786","version":1},"attestation_state":"computed","paper":{"title":"Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Guntur Budi Herwanto, Joanito Agili Lopo, Yunita Sari","submitted_at":"2026-06-10T08:20:09Z","abstract_excerpt":"Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this limitation, we propose an approach for fine-tuning LLMs on a low-resource language, Kupang Malay. Our approach involves designing a set of instructions by leveraging explicit lexical and semantic features from a bilingual dictionary, and introducing Continual Instruction Tuning (CIT), a training paradigm that enables iterative instruction-based training. Experimental results demonstrate that our model, named Lius, yields notab"},"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":"2606.11786","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-10T08:20:09Z","cross_cats_sorted":[],"title_canon_sha256":"062da5856aa149c72c10b3220f609dab7f18179add350cd1aa6175811d517811","abstract_canon_sha256":"c06bce01eca8851691f4b8a8f8a1952e9733cc148d69e70cd71101c27c88dce8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:10:07.749468Z","signature_b64":"A+KicJSg3S3E9xGM3Z9YMGtgxxqRiIlyv1Nn6fDFNp0HMbUMfo66xiVHVs7i8hQ4vvf6yjKMAS7c3PZC9542Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"460cfff50c48ea63a584a381131a0b9d1b83a7538e8ee6d75d2859a7c0ed1f09","last_reissued_at":"2026-06-11T01:10:07.748006Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:10:07.748006Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Lius: Translation Model Based Instructional Lingustic Using Continual Instruction Tuning In Kupang Malay","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Guntur Budi Herwanto, Joanito Agili Lopo, Yunita Sari","submitted_at":"2026-06-10T08:20:09Z","abstract_excerpt":"Large Language Models (LLMs) offer new potential for translation tasks but often experience performance degradation when handling low-resource languages. To address this limitation, we propose an approach for fine-tuning LLMs on a low-resource language, Kupang Malay. Our approach involves designing a set of instructions by leveraging explicit lexical and semantic features from a bilingual dictionary, and introducing Continual Instruction Tuning (CIT), a training paradigm that enables iterative instruction-based training. Experimental results demonstrate that our model, named Lius, yields notab"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11786","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/2606.11786/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":"2606.11786","created_at":"2026-06-11T01:10:07.748142+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.11786v1","created_at":"2026-06-11T01:10:07.748142+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11786","created_at":"2026-06-11T01:10:07.748142+00:00"},{"alias_kind":"pith_short_12","alias_value":"IYGP75IMJDVG","created_at":"2026-06-11T01:10:07.748142+00:00"},{"alias_kind":"pith_short_16","alias_value":"IYGP75IMJDVGHJME","created_at":"2026-06-11T01:10:07.748142+00:00"},{"alias_kind":"pith_short_8","alias_value":"IYGP75IM","created_at":"2026-06-11T01:10:07.748142+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/IYGP75IMJDVGHJMEUOARGGQLTU","json":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU.json","graph_json":"https://pith.science/api/pith-number/IYGP75IMJDVGHJMEUOARGGQLTU/graph.json","events_json":"https://pith.science/api/pith-number/IYGP75IMJDVGHJMEUOARGGQLTU/events.json","paper":"https://pith.science/paper/IYGP75IM"},"agent_actions":{"view_html":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU","download_json":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU.json","view_paper":"https://pith.science/paper/IYGP75IM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.11786&json=true","fetch_graph":"https://pith.science/api/pith-number/IYGP75IMJDVGHJMEUOARGGQLTU/graph.json","fetch_events":"https://pith.science/api/pith-number/IYGP75IMJDVGHJMEUOARGGQLTU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU/action/storage_attestation","attest_author":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU/action/author_attestation","sign_citation":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU/action/citation_signature","submit_replication":"https://pith.science/pith/IYGP75IMJDVGHJMEUOARGGQLTU/action/replication_record"}},"created_at":"2026-06-11T01:10:07.748142+00:00","updated_at":"2026-06-11T01:10:07.748142+00:00"}