{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WH3IV5HSPRHLX64VIP2JHXNUFF","short_pith_number":"pith:WH3IV5HS","canonical_record":{"source":{"id":"2410.17145","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-22T16:26:03Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f17078585a3f2a8316d607777ff0bb58ed2be74e3188b723ecf22d4518078c73","abstract_canon_sha256":"b54858a26b3487610e3092e9ca7ee3f03a0003235e6fc65cde16e2fd85434534"},"schema_version":"1.0"},"canonical_sha256":"b1f68af4f27c4ebbfb9543f493ddb4296f4a27995ce410261b1ba48f1d5e8466","source":{"kind":"arxiv","id":"2410.17145","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.17145","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"arxiv_version","alias_value":"2410.17145v1","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17145","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"pith_short_12","alias_value":"WH3IV5HSPRHL","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"pith_short_16","alias_value":"WH3IV5HSPRHLX64V","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"pith_short_8","alias_value":"WH3IV5HS","created_at":"2026-07-05T09:24:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WH3IV5HSPRHLX64VIP2JHXNUFF","target":"record","payload":{"canonical_record":{"source":{"id":"2410.17145","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-22T16:26:03Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"f17078585a3f2a8316d607777ff0bb58ed2be74e3188b723ecf22d4518078c73","abstract_canon_sha256":"b54858a26b3487610e3092e9ca7ee3f03a0003235e6fc65cde16e2fd85434534"},"schema_version":"1.0"},"canonical_sha256":"b1f68af4f27c4ebbfb9543f493ddb4296f4a27995ce410261b1ba48f1d5e8466","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:24:12.546923Z","signature_b64":"u+FdEmUK2/z+kQJWnKTQdSgOGelzKWbL4H3QygrKQDenmRKCr/sED9m0XGXImP+qfun9Lgbaiy4nSG++3TeADw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b1f68af4f27c4ebbfb9543f493ddb4296f4a27995ce410261b1ba48f1d5e8466","last_reissued_at":"2026-07-05T09:24:12.546462Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:24:12.546462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.17145","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:24:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wXqwx6oBN1td2cF7BbLLJlfeEkQmMpemtDq/qzemV5rY11ialth2cykKeoBaXXfWJlle9Y9SvMjNkEpIMlMCAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:24:15.902235Z"},"content_sha256":"f6f95ed4d96e7ff17781ed8939c0df5c01075037cfae40a47a427dea0a8c4cb0","schema_version":"1.0","event_id":"sha256:f6f95ed4d96e7ff17781ed8939c0df5c01075037cfae40a47a427dea0a8c4cb0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WH3IV5HSPRHLX64VIP2JHXNUFF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CL","authors_text":"Amrest Chinkamol, Jiramet Kinchagawat, Jirat Chiaranaipanich, Jitkapat Sawatphol, Krittamate Tiankanon, Naiyarat Hanmatheekuna, Parinthapat Pengpun, Peerat Limkonchotiwat, Piyalitt Ittichaiwong","submitted_at":"2024-10-22T16:26:03Z","abstract_excerpt":"Large language models (LLMs) perform well on common tasks but struggle with generalization in low-resource and low-computation settings. We examine this limitation by testing various LLMs and specialized translation models on English-Thai machine translation and code-switching datasets. Our findings reveal that under more strict computational constraints, such as 4-bit quantization, LLMs fail to translate effectively. In contrast, specialized models, with comparable or lower computational requirements, consistently outperform LLMs. This underscores the importance of specialized models for main"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17145","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/2410.17145/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":"2026-07-05T09:24:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rnjd0QfZIqfOe1L8Lbp534giXTe7oEhAccNZJ98Qrmee1qVt9UcfnKckvwUX+JJyA9JTGbN98KABgQ056XT9Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T02:24:15.902624Z"},"content_sha256":"7e823a3e7d52772257c35b39431e8cf1cb5f1a737b48a568cd7b0ebc07ca6f9c","schema_version":"1.0","event_id":"sha256:7e823a3e7d52772257c35b39431e8cf1cb5f1a737b48a568cd7b0ebc07ca6f9c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WH3IV5HSPRHLX64VIP2JHXNUFF/bundle.json","state_url":"https://pith.science/pith/WH3IV5HSPRHLX64VIP2JHXNUFF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WH3IV5HSPRHLX64VIP2JHXNUFF/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":"2026-07-09T02:24:15Z","links":{"resolver":"https://pith.science/pith/WH3IV5HSPRHLX64VIP2JHXNUFF","bundle":"https://pith.science/pith/WH3IV5HSPRHLX64VIP2JHXNUFF/bundle.json","state":"https://pith.science/pith/WH3IV5HSPRHLX64VIP2JHXNUFF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WH3IV5HSPRHLX64VIP2JHXNUFF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WH3IV5HSPRHLX64VIP2JHXNUFF","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":"b54858a26b3487610e3092e9ca7ee3f03a0003235e6fc65cde16e2fd85434534","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-22T16:26:03Z","title_canon_sha256":"f17078585a3f2a8316d607777ff0bb58ed2be74e3188b723ecf22d4518078c73"},"schema_version":"1.0","source":{"id":"2410.17145","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.17145","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"arxiv_version","alias_value":"2410.17145v1","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.17145","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"pith_short_12","alias_value":"WH3IV5HSPRHL","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"pith_short_16","alias_value":"WH3IV5HSPRHLX64V","created_at":"2026-07-05T09:24:12Z"},{"alias_kind":"pith_short_8","alias_value":"WH3IV5HS","created_at":"2026-07-05T09:24:12Z"}],"graph_snapshots":[{"event_id":"sha256:7e823a3e7d52772257c35b39431e8cf1cb5f1a737b48a568cd7b0ebc07ca6f9c","target":"graph","created_at":"2026-07-05T09:24:12Z","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/2410.17145/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) perform well on common tasks but struggle with generalization in low-resource and low-computation settings. We examine this limitation by testing various LLMs and specialized translation models on English-Thai machine translation and code-switching datasets. Our findings reveal that under more strict computational constraints, such as 4-bit quantization, LLMs fail to translate effectively. In contrast, specialized models, with comparable or lower computational requirements, consistently outperform LLMs. This underscores the importance of specialized models for main","authors_text":"Amrest Chinkamol, Jiramet Kinchagawat, Jirat Chiaranaipanich, Jitkapat Sawatphol, Krittamate Tiankanon, Naiyarat Hanmatheekuna, Parinthapat Pengpun, Peerat Limkonchotiwat, Piyalitt Ittichaiwong","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-22T16:26:03Z","title":"Can General-Purpose Large Language Models Generalize to English-Thai Machine Translation ?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.17145","kind":"arxiv","version":1},"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:f6f95ed4d96e7ff17781ed8939c0df5c01075037cfae40a47a427dea0a8c4cb0","target":"record","created_at":"2026-07-05T09:24:12Z","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":"b54858a26b3487610e3092e9ca7ee3f03a0003235e6fc65cde16e2fd85434534","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-22T16:26:03Z","title_canon_sha256":"f17078585a3f2a8316d607777ff0bb58ed2be74e3188b723ecf22d4518078c73"},"schema_version":"1.0","source":{"id":"2410.17145","kind":"arxiv","version":1}},"canonical_sha256":"b1f68af4f27c4ebbfb9543f493ddb4296f4a27995ce410261b1ba48f1d5e8466","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b1f68af4f27c4ebbfb9543f493ddb4296f4a27995ce410261b1ba48f1d5e8466","first_computed_at":"2026-07-05T09:24:12.546462Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:24:12.546462Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u+FdEmUK2/z+kQJWnKTQdSgOGelzKWbL4H3QygrKQDenmRKCr/sED9m0XGXImP+qfun9Lgbaiy4nSG++3TeADw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:24:12.546923Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.17145","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f6f95ed4d96e7ff17781ed8939c0df5c01075037cfae40a47a427dea0a8c4cb0","sha256:7e823a3e7d52772257c35b39431e8cf1cb5f1a737b48a568cd7b0ebc07ca6f9c"],"state_sha256":"9b2553453bf981eb83d7566937fa4864b3c657c2e722349fab6a20922d44414b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fqw6JZ3R/3QJWSQ/VSQvqJtDvkbpDsTehk9xyYbL4rasQQW2Sfp6KeVkKk6cZRo+ZFsy6nrn1+I/U9PQEPQ/DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T02:24:15.904551Z","bundle_sha256":"3931edc649c6aa9addfde62417ca63914074c9b22c076b2929f7cc78d04346b9"}}