This paper presents Typhoon 2, Thai-optimized models for text, vision, and audio. It outlines methods like continual pre-training and post-training to enhance Thai performance, with evaluation across tasks. The series includes models from 1 to 70 billion parameters, safety tools, and advances in document understanding and speech processing.

The Typhoon series introduces Thai LLMs optimized for low-resource challenges, using continual training and ThaiExam for evaluation. Fine-tuned for Thai tasks, Typhoon outperforms open-source models and rivals GPT-3.5 in Thai, with greater efficiency.

CrossCheckGPT introduces a reference-free method for ranking hallucinations in multimodal foundation models, leveraging cross-system consistency as a measure of robustness. Applicable across domains and tasks, it uses explicit and implicit consistency metrics to assess hallucination levels. The method demonstrates high correlation with human judgments and supports new benchmarks, including the first audio-visual hallucination benchmark, AVHalluBench. In collaboration with University of Cambridge, Tsinghua University.

This paper evaluates audio language models in low-resource languages, using Thai as an example, revealing their limitations despite multilingual pretraining. It explores data mixtures to optimize models for both a target language and English, integrating audio comprehension and speech instruction-following into a unified framework. The proposed model, Typhoon-Audio, significantly outperforms open-source models and rivals state-of-the-art systems like Gemini-1.5-Pro in both English and Thai.