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Large Language Models (LLMs) excel in various tasks, including text generation, translation, and summarization. However, a growing challenge within NLP is how these models can effectively interact with external tools to perform tasks beyond their inherent capabilities. This challenge is particularly relevant in real-world applications where LLMs must fetch real-time data, perform complex calculations, or…
Document Visual Question Answering (DocVQA) is a branch of visual question answering that focuses on answering queries about the contents of documents. These documents can take several forms, including scanned photographs, PDFs, and digital documents with text and visual features. However, there are few datasets for DocVQA because collecting and annotating the data is complicated.…
Recent advances in large language models (LLMs) have made it possible to use LLM agents in many areas, including safety-critical ones like finance, healthcare, and self-driving cars. Usually, these agents use an LLM to understand tasks for making plans, and they can use external tools, like third-party APIs, to carry out those plans. However, their…
Effectively evaluating document instruction data for training large language models (LLMs) and multimodal large language models (MLLMs) in document visual question answering (VQA) presents a significant challenge. Existing methods are primarily text-oriented, focusing on the textual content of instructions rather than the execution process, which limits their ability to comprehensively assess the quality and efficacy…
Securing their products is a challenge for businesses. Teams are inundated with false positives from current Static Application Security Testing (SAST) technologies, and those identifying vulnerabilities cannot be fixed. Meet ZeroPath, a GitHub app that detects, verifies, and issues pull requests for security vulnerabilities in your code. The ZeroPath tool not only automatically identifies vulnerabilities…
On-call shifts can be very stressful for engineers. When something goes wrong in a system, the person on call has to figure out the problem and fix it quickly. This often means going through lots of logs and data, which takes time and can be challenging, especially during off-hours. Getting to the root cause of…
AI and ML in Untargeted Metabolomics and Exposomics: Metabolomics employs a high-throughput approach to measure a variety of metabolites and small molecules in biological samples, providing crucial insights into human health and disease. One application, untargeted metabolomics, allows for an unbiased global analysis of the metabolome, identifying key metabolites that contribute to or indicate health…
A major development in artificial intelligence, multimodal large language models (MLLMs) combine verbal and visual comprehension to produce more accurate representations of multimodal inputs. Through the integration of data from multiple sources, including text and images, these models improve understanding of intricate relationships between various modalities. Because of this integration, sophisticated tasks requiring a thorough…
The recent development of large language models (LLMs) has transformed the field of Natural Language Processing (NLP). LLMs show human-level performance in many professional and academic fields, showing a great understanding of language rules and patterns. However, they often struggle with reasoning reliably and flexibly. This problem likely comes from the way transformers, the underlying…
The rapid advancement of Artificial Intelligence (AI) and Machine Learning (ML) has highlighted the critical need for large, diverse, and high-quality datasets to train and evaluate foundation models. However, acquiring such datasets presents significant challenges, including data scarcity, privacy concerns, and high data collection and annotation costs. Artificial (synthetic) data has emerged as a promising…