Tech billionaires seem to be doom prepping. Should we all be worried? Mark Zuckerberg is said to have started work on Koolau Ranch, his sprawling 1,400-acre compound on the Hawaiian island of Kauai, as far back as 2014. It is set to include a shelter, complete with its own energy and food supplies, though the carpenters and electricians working on the site were banned from talking about it by non-disclosure agreements, according to a report by Wired magazine. A six-foot wall blocked the project from view of a nearby road.

Generative AI and large language models hold great promise in enhancing programming education by generating individualized feedback and hints for learners. Recent works have primarily focused on improving the quality of generated feedback to achieve human tutors' quality.

A recent line of work aims at specially prompting LLMs to review their own generations and generate meaningful natural language feedback, which can subsequently be used to refine them [Madaan et al.,

The advent of large language models (LLMs) has revolutionized the field of natural language processing, yet they might be attacked to produce harmful content. Despite efforts to ethically align LLMs, these are often fragile and can be circumvented by jailbreaking attacks through optimized or manual adversarial prompts.

We propose an unsupervised adaptation framework, Self-T Aught Recognizer (ST AR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) systems in diverse target domains, such as noise and accents. ST AR is developed for prevalent speech foundation models based on Transformer-related architecture with auto-regressive decoding (e.g., Whisper, Canary; SeamlessM4T).