The rapid progression of Large Language Models (LLMs) is a pivotal milestone in the evolution of artificial intelligence. In recent years, we have witnessed a surge in the development and public accessibility of well-trained LLMs in English and other languages, including Japanese. This expansion underscores a global effort to democratize AI capabilities across linguistic and cultural boundaries.
Building upon the advancements in LLMs, novel approaches have emerged for constructing Vision Language Models (VLMs), which integrate image encoders into language models. These VLMs hold promise in their capacity to understand and generate textual descriptions of visual content. Various evaluation metrics have been proposed to gauge their effectiveness, encompassing tasks such as image captioning, similarity scoring between images and text, and visual question answering (VQA). However, it’s notable that most high-performing VLMs are trained and evaluated predominantly on English-centric datasets.
The need for robust evaluation methodologies becomes increasingly urgent as the demand for non-English models burgeons, particularly in languages like Japanese. Recognizing this imperative, a new evaluation benchmark called the Japanese Heron-Bench has been introduced. This benchmark comprises a meticulously curated dataset of images and contextually relevant questions tailored to the Japanese language and culture. Through this benchmark, the efficacy of VLMs in comprehending visual scenes and responding to queries within the Japanese context can be thoroughly scrutinized.
In tandem with establishing the Japanese Heron-Bench, efforts have been directed toward developing Japanese VLMs trained on Japanese image-text pairs using existing Japanese LLMs. This serves as a foundational step in bridging the gap between LLMs and VLMs in the Japanese linguistic landscape. Such models’ availability facilitates research and fosters innovation in diverse applications ranging from language understanding to visual comprehension.
Despite the strides made in evaluation methodologies, inherent limitations persist. For instance, the accuracy of assessments may be compromised by the performance disparities between languages in LLMs. This is particularly salient in the case of Japanese, where the language proficiency of models may differ from that of English. Additionally, concerns regarding safety aspects such as misinformation, bias, or toxicity in generated content warrant further exploration in evaluation metrics.
In conclusion, while introducing the Japanese Heron-Bench and Japanese VLMs represents significant strides toward comprehensive evaluation and utilization of VLMs in non-English contexts, challenges remain to be addressed. In the future, researchers will research evaluation metrics, and safety considerations will be pivotal in ensuring VLMs’ efficacy, reliability, and ethical deployment across diverse linguistic and cultural landscapes.
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Arshad is an intern at MarktechPost. He is currently pursuing his Int. MSc Physics from the Indian Institute of Technology Kharagpur. Understanding things to the fundamental level leads to new discoveries which lead to advancement in technology. He is passionate about understanding the nature fundamentally with the help of tools like mathematical models, ML models and AI.
