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Evaluating LLMs as versatile agents is crucial for their integration into practical applications. However, existing evaluation frameworks face challenges in benchmarking diverse scenarios, maintaining partially observable environments, and capturing multi-round interactions. Current assessments often focus on a simplified final success rate metric, providing limited insights into the complex processes. The complexity of agent tasks, involving…

Transformers have found widespread application in diverse tasks spanning text classification, map construction, object detection, point cloud analysis, and audio spectrogram recognition. Their versatility extends to multimodal tasks, exemplified by CLIP’s use of image-text pairs for superior image recognition. This underscores transformers’ efficacy in establishing universal sequence-to-sequence modeling, creating embeddings that unify data representation across…

Large language models (LLMs) based on transformer architectures have emerged in recent years. Models such as Chat-GPT and LLaMA-2 demonstrate how the parameters of LLMs have rapidly increased, ranging from several billion to tens of trillions. Although LLMs are very good generators, they have trouble with inference delay since there is a lot of computing…

Artificial intelligence is widely useful in environment-related fields. Recently, there has been increasing research on using AI in carbon capture technology. Carbon capture technology is critical in tackling climate change by trapping carbon dioxide (CO2) emissions from power plants. However, the current carbon capture systems are inefficient and may consume significant energy. Consequently, researchers from…

One of the more intriguing developments in the dynamic field of computer vision is the efficient processing of visual data, which is essential for applications ranging from automated image analysis to the development of intelligent systems. A pressing challenge in this area is interpreting complex visual information, particularly in reconstructing detailed images from partial data….

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,…