Papers by Lalita Lowphansirikul
McCrolin: Multi-consistency Cross-lingual Training for Retrieval Question Answering (2024.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Potsawee Manakul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Existing approaches struggle with consistency across multiple languages and multi-size input scenarios. |
| Approach: | They propose a cross-lingual training framework that leverages multi-task learning to enhance cross-linguistic consistency and ranking stability. |
| Outcome: | The proposed training framework outperforms competitors on various input sizes and architectures. |
ConGen: Unsupervised Control and Generalization Distillation For Sentence Representation (2022.findings-emnlp)
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Peerat Limkonchotiwat, Wuttikorn Ponwitayarat, Lalita Lowphansirikul, Can Udomcharoenchaikit, Ekapol Chuangsuwanich, Sarana Nutanong
| Challenge: | Sentence representations are essential in many NLP tasks operating at the sentence level. |
| Approach: | They propose an unsupervised sentence representation method to reduce the supervised-unsupervised performance gap for smaller models. |
| Outcome: | The proposed method outperforms supervised training on STS, text classification, and natural language inference tasks on smaller models. |
WangchanThaiInstruct: An instruction-following Dataset for Culture-Aware, Multitask, and Multi-domain Evaluation in Thai (2025.emnlp-main)
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Peerat Limkonchotiwat, Pume Tuchinda, Lalita Lowphansirikul, Surapon Nonesung, Panuthep Tasawong, Alham Fikri Aji, Can Udomcharoenchaikit, Sarana Nutanong
| Challenge: | Existing benchmarks for large language models rely on translations, missing cultural and domain specificity. |
| Approach: | They present a human-authored dataset for evaluation and instruction tuning in Thai . findings highlight need for culturally and professionally grounded instruction data . |
| Outcome: | a human-authored dataset for evaluation and instruction tuning in Thai outperforms translation-based models . findings highlight need for culturally and professionally grounded instruction data . |