Parinthapat Pengpun


2024

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SICAR at RRG2024: GPU Poor’s Guide to Radiology Report Generation
Kiartnarin Udomlapsakul | Parinthapat Pengpun | Tossaporn Saengja | Kanyakorn Veerakanjana | Krittamate Tiankanon | Pitikorn Khlaisamniang | Pasit Supholkhan | Amrest Chinkamol | Pubordee Aussavavirojekul | Hirunkul Phimsiri | Tara Sripo | Chiraphat Boonnag | Trongtum Tongdee | Thanongchai Siriapisith | Pairash Saiviroonporn | Jiramet Kinchagawat | Piyalitt Ittichaiwong
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing

Radiology report generation (RRG) aims to create free-text radiology reports from clinical imaging. Our solution employs a lightweight multimodal language model (MLLM) enhanced with a two-stage post-processing strategy, utilizing a Large Language Model (LLM) to boost diagnostic accuracy and ensure patient safety. We introduce the “First, Do No Harm” SafetyNet, which incorporates Xraydar, an advanced X-ray classification model, to cross-verify the model outputs and specifically address false negatives from the MLLM. This comprehensive approach combines the efficiency of lightweight models with the robustness of thorough post-processing techniques, offering a reliable solution for radiology report generation. Our system achieved fourth place on the F1-Radgraph metric for findings generation in the Radiology Report Generation Shared Task (RRG24).

2023

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Cross-Lingual Data Augmentation For Thai Question-Answering
Parinthapat Pengpun | Can Udomcharoenchaikit | Weerayut Buaphet | Peerat Limkonchotiwat
Proceedings of the 1st GenBench Workshop on (Benchmarking) Generalisation in NLP

This paper presents an innovative data augmentation framework with data quality control designed to enhance the robustness of Question Answering (QA) models in low-resource languages, particularly Thai. Recognizing the challenges posed by the scarcity and quality of training data, we leverage data augmentation techniques in both monolingual and cross-lingual settings. Our approach augments and enriches the original dataset, thereby increasing its linguistic diversity and robustness. We evaluate the robustness of our framework on Machine Reading Comprehension, and the experimental results illustrate the potential of data augmentation to effectively increase training data and improve model generalization in low-resource language settings, offering a promising direction for the data augmentation manner.