Abstract Over the past decades, machine learning has primarily relied on labeled data, with success often depending on the availability of vast, high-quality annotations and the assumption that test conditions mirror training conditions. In contrast,[…]

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Abstract Human preferences have been the main component in guiding pretrained AI. In this talk, I argue that human preferences are often insufficient for complementing human intelligence and demonstrate the key role of human goals[…]

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 Abstract The widespread deployment of speech technologies has highlighted the urgent need to protect the personal identifying information of users of these technologies. In this talk, we will first introduce the currently prevailing definition for[…]

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Abstract In this talk, we will discuss two of our recent works aimed at enhancing the task-specific performance of Large Language Models (LLMs). In the first part, we explore the generation of high-quality synthetic data[…]

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Abstract The shared universal properties of human languages have been at the heart of many linguistic debates for decades. A big part of these debates are two core questions: (1) learnability — can all linguistic[…]

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Abstract The rapid advancement of large language models (LLMs) has unlocked a myriad of possibilities for positive societal impact, ranging from enhancing accessibility and communication to supporting disaster response and public health initiatives. However, the[…]

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Abstract Self-consistency (SC) has been demonstrated to enhance the performance of large language models (LLMs) across various tasks and domains involving short content. However, does this evidence support its effectiveness for long-context problems? In this talk, we examine the[…]

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