ThLM

ThLM is an acronym used to denote a family of language models designed to process Thai language text. These models aim to improve natural language understanding and generation for Thai by leveraging large-scale pretraining on Thai corpora and adapting transformer architectures to the linguistic characteristics of Thai, such as word segmentation and script handling. The term is used across academic, industry, and open-source projects that target Thai NLP tasks.

ThLM models are typically built on transformer architectures, including encoder-only, decoder-only, or encoder-decoder variants. They are pretrained on Thai text using objectives such as masked language modeling or causal language modeling, and they rely on Thai-specific tokenization strategies, which may involve subword units or word-level segmentation. Training data often combines news, literature, websites, and social media while emphasizing data quality and genre coverage.

Applications include machine translation to and from Thai, sentiment analysis, named entity recognition, question answering, chatbots, and other text-generation tasks. In practice, ThLMs are deployed in educational tools, digital assistants, search and information retrieval, and content moderation where Thai language support is needed.

Evaluation typically uses standard Thai NLP benchmarks and real-world tasks to measure accuracy, fluency, and robustness. Challenges for ThLMs include limited development data for some dialects, code-switching with English, handling formal vs. colloquial Thai, and potential biases inherited from training corpora.

Outlook: ThLMs are part of broader efforts to extend NLP capabilities to Thai and other languages with unique scripts and linguistic features. Ongoing work focuses on multilingual efficiency, model alignment with safety standards, and expanding coverage of dialects and registers. See also Thai natural language processing, transformer-based language models, and language model evaluation.