Abstract Humans don’t learn intelligence—they develop it. Biological life on Earth benefits from innate cognitive primitives encoded in its genes. Cats don’t learn to backflip, just as birds don’t learn to fly. Likewise, humans don’t[…]
Abstract Large language models (LLMs) have rapidly and impressively taken over many tasks once handled by human annotators in constructing text datasets. But does this mean we no longer need humans in the annotation loop?[…]
Abstract Pre-trained language models form the basis for much of modern NLP, and while the basic pre-training recipe is well known, many details of model development are hidden in secretive research labs. Based on experience[…]
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 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[…]
