Multimodal Art Projection (M-A-P) researchers have introduced FineFineWeb, a large open-source automatic classification system for fine-grained web data. The project decomposes the deduplicated Fineweb into 67 unique categories with extensive seed data. Moreover, a comprehensive correlation analysis between vertical categories and common benchmarks and detailed URL and content distribution analysis are conducted. The system provides…
Artificial intelligence has progressed from handling atomic tasks to addressing intricate, real-world problems requiring the integration of multiple specialized models. This approach, known as AI pipelines, allows for seamless task transitions by connecting different models to process diverse data inputs and outputs. These pipelines enable complex applications like multilingual video dubbing, multimodal content moderation, and…
The advent of automatic speech recognition (ASR) technologies has changed the way individuals interact with digital devices. Despite their capabilities, these systems often demand significant computational power and resources. This makes them inaccessible to users with constrained devices or limited access to cloud-based solutions. This disparity underscores an urgent need for innovations that deliver high-quality…
Artificial intelligence (AI) is reshaping the way we approach everyday tasks, simplifying processes, and unlocking new levels of efficiency. AI tools enhance productivity and offer innovative solutions to a wide range of challenges, from managing daily routines to improving communication and decision-making. Whether it’s automating repetitive chores, organizing schedules, or personalizing experiences, AI is becoming…
Designing antibodies with high specificity and binding affinity to diverse therapeutic antigens remains a significant challenge in drug development. Current methods struggle to effectively generate complementarity-determining regions (CDRs) responsible for antigen binding, especially the highly variable heavy chain CDR3 (HCDR3). These difficulties are mainly due to poor generalization of the already existing computational models to…
The field of neural network architectures has witnessed rapid advancements as researchers explore innovative ways to enhance computational efficiency while maintaining or improving model performance. Traditional dense networks rely heavily on computationally expensive matrix operations to encode and store information. This reliance poses challenges when scaling these models for real-world applications that demand extensive knowledge…
Since the release of BERT in 2018, encoder-only transformer models have been widely used in natural language processing (NLP) applications due to their efficiency in retrieval and classification tasks. However, these models face notable limitations in contemporary applications. Their sequence length, capped at 512 tokens, hampers their ability to handle long-context tasks effectively. Furthermore, their…
Large Language Models (LLMs) have become a cornerstone of artificial intelligence, driving advancements in natural language processing and decision-making tasks. However, their extensive power demands, resulting from high computational overhead and frequent external memory access, significantly hinder their scalability and deployment, especially in energy-constrained environments such as edge devices. This escalates the cost of operation…
Despite the transformative potential of large language models (LLMs), these models face significant challenges in generating contextually accurate responses faithful to the provided input. Ensuring factuality in LLM outputs is particularly critical in tasks requiring responses grounded in lengthy, complex documents, which form the basis for advancing their applications in research, education, and industry. One…
For education research, access to high-quality educational resources is critical for learners and educators. Often perceived as one of the most challenging subjects, mathematics requires clear explanations and well-structured resources to make learning more effective. However, creating and curating datasets focusing on mathematical education remains a formidable challenge. Many datasets for training machine learning models…