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Thai Generalized Alignment Task (TGAT): A Corpus and Comparative Study for Hallucination Detection in Thai
(2025-01-01)
Wangwon, Sivakorn
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Pitijaroonpong, Rapepong
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Chuangkrud, Piyawat
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Damrongrat, Chaianun
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Kongyoung, Sarawoot
Large Language Models (LLMs) face critical reliability challenges due to hallucination-the generation of factually inaccurate content. While hallucination detection has advanced for major languages, Thai remains underserved, lacking specialized alignment models and datasets. We introduce the Thai Generalized Alignment Task (TGAT), a seven-subtask corpus tailored for Thai hallucination detection, spanning Fact Verification, Natural Language Inference, Information Retrieval, Question Answering, Summarization, Semantic Textual Similarity, and Paraphrase Identification. We conduct a comparative study across transformer families-encoder-only, encoder-decoder, and decoder-only-evaluated on the test set of each sub-task to analyze architectural compatibility and generalization for Thai hallucination detection. We also perform an ablation study to quantify how the presence of each subtask dataset affects the overall average performance, clarifying the role of dataset composition. Our experiments show that decoder-only models consistently outperform encoder-decoder and encoder-only alternatives under a standardized zero-shot prompting setup, establishing strong baselines and offering guidance for model selection and dataset design in low-resource settings.
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Pantip Multi-turn Datasets Generating from Thai Large Social Platform Forum Using Sentence Similarity Techniques
(2024-01-01)
Sae-Oueng, Anon
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Kerdthaisong, Kun
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Sukhantharat, Kittisak
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Phasook, Pakawat
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Chuangkrud, Piyawat
Fine-tuning Large Language Models (LLMs) for specific domains is crucial. However, lack of Thai open dialogues presents a major challenge. For the major challenge, this study proposes a novel methodology for extracting and constructing multi-turn conversational data from existing Thai large social platform, named Pantip. Our approach implements semantic matching algorithms to identify and compile both single-turn and multi-turn dialogues. By employing a cosine similarity threshold ≥ 0.3, we yields contextually coherent conversation pairs directly from the source data. The outcome dataset represents real Thai conversation styles, which could improve how accurately fine-tuned language models reflect Thai social and linguistic norms. Our approach introduces a new way to use existing publicly available data to create training datasets, which is especially valuable for languages with limited resources. Chaotic Pantip datasets can be contributed to the development of more culturally attuned and linguistically precise Thai language models, potentially advancing culturally-specific natural language processing.
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When are Latent Topics Useful for Text Mining?: Enriching Bag-of-Words Representations with Information Extraction in Thai News Articles
(2023-01-01)
Kanungsukkasem, Nont
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Chuangkrud, Piyawat
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Pitichotchokphokhin, Pimpitcha
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Damrongrat, Chaianun
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Leelanupab, Teerapong
The Bag-of-Words (BOW) model is simple but one of the successful representations of text documents. This model, however, suffers from the sparse matrix, in which most of the elements are zero. Topic modeling is an unsupervised learning method that can represent text documents in a low-dimensional space. Latent Dirichlet Allocation (LDA) is a topic modeling technique used for topic extraction and data exploration, with interpretable output. This paper presents a thorough study of potential benefits of applying LDA, as a feature extraction, to topic discovery and document classification in Thai news articles, comparing with TF–IDF and Word2Vec. We also studied how much of the top Thai terms extracted from LDA with the different numbers of topics can be interpretable and meaningful, and can be a representative of the corpus. Besides, a set of Topic Coherence measures were included in our study to estimate the degree of semantic similarity of extracted topics. To compare the performance and optimization time of classification of features from the different feature extraction methods, various types of classifiers, e.g., Logistic Regression, Random Forest, XGBoosting, etc., were experimented.
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A Non-Entity Approach for Intent-Based Classification: A Case Study of Thai News
(2022-01-01)
Damrongrat, Chaianun
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Sangkeettrakarn, Chatchawal
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Piyatumrong, Apivadee
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Trankultaweekoon, Kanokorn
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Seekhem, Pachit
Conjunction and stop words are normally ignored in text classification task that is content-based, such as classifying news into entertainment or sports. However, they are useful in this study, since the content and the intention of the document are independent. This paper studies intent-based classification that specifically desires to classify the author's intention of Thai news article into three intents, 'inform', 'explain', and 'give solution'. These three intents subtly co-exist with the content of the article and thus is our classification challenge. Our experiments confirm that intent-based classification needs a different approach from those techniques used for content-based classification. Accordingly, we propose a new pipeline for Thai intent-based classification such that conjunction and others can play a significant role above entity. Our contributions include (1) proving the need for a new methodology to handle intent-based classification and (2) proposing the Non-Entity data processing approach to managing intent-based classification problems. The proposed methodology shows partially promising results. Nonetheless, flags for enhancement are also discussed in the conclusion for future works.
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Discover Underlying Topics in Thai News Articles: A Comparative Study of Probabilistic and Matrix Factorization Approaches
(2020-06-01)
Pitichotchokphokhin, Pimpitcha
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Chuangkrud, Piyawat
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Kalakan, Kongkan
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Suntisrivaraporn, Boontawee
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Leelanupab, Teerapong
Topic modeling is an unsupervised learning approach, which can automatically discover the hidden thematic structure in text documents. For text mining, topic modeling is a language-independent technique that disregards grammar and word order. Apart from semantic and structural issues, Thai language is typically considered more complex than others. Due to the lack of word delimiter and a surfeit of composite words. Errors from word tokenization can create significant problems for any post processes of text, such as document retrieval, sentiment analysis, machine translation, etc., adversely decreasing the performance of text applications. Despite a strong correlation between word ordering and semantic meaning, topic modeling has been widely reported that it can extract latent information, aka. latent topic or latent semantic, encoded in documents. Although there were few previous research works on studying topic modeling in Thai language, they mostly focused on upstream processes of Natural Language Processing (NLP) in, for example, applying a refined stop-word list to, or adding N-gram on a single specific topic modeling method. To our knowledge, this paper is the first to explore different topic modeling approaches, i.e., Latent Dirichlet Allocation (LDA) and Nonnegative Metrix Factorization (NMF), in Thai Language to compare their coherence. We also employ and compare a set of state-of-the-art evaluation metrics based on Topic Coherence.

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