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As Large Language Models (LLMs) gain prominence in high-stakes applications, understanding their decision-making processes becomes crucial to mitigate potential risks. The inherent opacity of these models has fueled interpretability research, leveraging the unique advantages of artificial neural networks—being observable and deterministic—for empirical scrutiny. A comprehensive understanding of these models not only enhances our knowledge but…

Existing web agents face limitations that stem from the fact that these agents often rely on a single input modality and are tested in controlled environments, like web simulators or static snapshots, which do not accurately reflect the complexity and dynamic nature of real-world web interactions. This significantly restricts their applicability and effectiveness in real-world…

Vision-Language Models (VLMs) are Artificial Intelligence (AI) systems that can interpret and comprehend visual and written inputs. Incorporating Large Language Models (LLMs) into VLMs has enhanced their comprehension of intricate inputs. Though VLMs have made encouraging development and gained significant popularity, there are still limitations regarding their effectiveness in difficult settings. The core of VLMs,…

Integrating artificial intelligence into software products marks a revolutionary shift in the technology field. As businesses race to incorporate advanced AI features, the creation of ‘product copilots’ has gained traction. These tools enable users to interact with software through natural language, significantly enhancing the user experience. This presents a new set of challenges for software…

We’re developing a blueprint for evaluating the risk that a large language model (LLM) could aid someone in creating a biological threat. In an evaluation involving both biology experts and students, we found that GPT-4 provides at most a mild uplift in biological threat creation accuracy. While this uplift is not large enough to be conclusive,…

In technical group chats, particularly those linked to open-source projects, the challenge of managing the flood of messages and ensuring relevant, high-quality responses is ever-present. Open-source project communities on instant messaging platforms often grapple with the influx of relevant and irrelevant messages. Traditional approaches, including basic automated responses and manual interventions, must be revised to…

Data scientists and ML engineers often need help to build full-stack applications. These professionals typically have a firm grasp of data and AI algorithms. Still, they may need more skills or time to learn new languages or frameworks to create user-friendly web applications. This disconnect can hinder the implementation of their data-driven solutions, making it…

Large Language Models (LLMs) have become increasingly pivotal in the burgeoning field of artificial intelligence, especially in data management. These models, which are based on advanced machine learning algorithms, have the potential to streamline and enhance data processing tasks significantly. However, integrating LLMs into repetitive data generation pipelines is challenging, mainly due to their unpredictable…

Recent developments in Multi-Modal (MM) pre-training have helped enhance the capacity of Machine Learning (ML) models to handle and comprehend a variety of data types, including text, pictures, audio, and video. The integration of Large Language Models (LLMs) with multimodal data processing has led to the creation of sophisticated MM-LLMs (MultiModal Large Language Models). In…

Large language models (LLMs) have made impressive advancements in generating coherent text for various activities and domains, including grammatical error correction (GEC), text simplification, paraphrasing, and style transfer. One of the emerging skills of LLMs is their ability to generalize and perform tasks that they have never seen before. To achieve this, LLMs are fine-tuned…