Abstract Open source generative models like OpenGPT2, BLOOM, and others have been pivotal in advancing AI technology. These models leverage extensive text data to achieve advanced linguistic capabilities. However, the trend towards proprietary tools and[…]
Abstract Controlling language models is key to unlocking their full potential and making them useful for downstream tasks. Successfully deploying these models often requires both task-specific customization and rigorous auditing of their behavior. In this[…]
Abstract With language models becoming increasingly embedded in our digital interactions, concerns over data privacy and information leakage are becoming more apparent. The lack of regulation and transparency in how pipelines involving LLMs collect and[…]
Abstract While state-of-the-art models for machine translation and natural language understanding perform well on high-resource members of (some of) the world’s language families, they degrade on closely-related languages, dialects, and variants of these languages, which[…]
Abstract Pretrained language models have brought revolutionary progress to information-seeking in the English world. While the advance is exciting, how to transfer such progress into non-English, especially lower resource languages, presents new challenges that require[…]
Abstract The paradigm of training large-scale foundation models has driven significant advancements in multimodal AI. However, pursuing further performance gains solely through model scaling is becoming impractical due to rising computational costs and resource limitations.[…]
Abstract Information is one of the most powerful forces in today’s digital era, shaping decisions at every level of society. While the ways we seek and access information have evolved over time, core challenges—such as[…]
