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Language Learning Models (LLMs), which are very good at reasoning and coming up with good answers, are sometimes honest about their mistakes and tend to hallucinate when asked questions they haven’t seen before. When the responses are more than just one token, it becomes much more important to determine how to get trustworthy confidence estimations…
Cultural accumulation, the ability to learn skills and accumulate knowledge across generations, is considered a key driver of human success. However, current methodologies in artificial learning systems, such as deep reinforcement learning (RL), typically frame the learning problem as occurring over a single “lifetime.” This approach fails to capture the generational and open-ended nature of…
Most neural network topologies heavily rely on matrix multiplication (MatMul), primarily because it is essential to many basic processes. Vector-matrix multiplication (VMM) is commonly used by dense layers in neural networks, and matrix-matrix multiplication (MMM) is used by self-attention mechanisms. The heavy dependence on MatMul can largely be attributed to GPU optimization for these kinds…
OpenAI recently announced a revolutionary feature called GPTs. The concept of GPTs is very simple to explain: GPTs mean you can create a custom version of ChatGPT by combining instructions, extra knowledge on the subject matter, and some skills. Basically, GPTs are custom versions of ChatGPT that specialize in a specific subject matter, which could…
Large Language Models (LLMs) have advanced significantly in recent years. Models like ChatGPT and GPT-4 allow users to interact with and elicit natural language responses. To improve the human-machine interaction and accuracy of LLMs, it is essential to have a method to evaluate these interactions dynamically. While LLMs have shown remarkable capabilities in generating text,…
Agents based on LLMs hold promise for accelerating scientific discovery, especially in biomedical research. They leverage extensive background knowledge to design and interpret experiments, particularly useful for identifying drug targets through CRISPR-based genetic perturbation. Despite their potential, LLM-based agents have yet to be fully utilized in designing biological experiments. Challenges include balancing freedom in exploring…
Multimodal learning is a rapidly evolving field focusing on training models to understand and generate content across various modalities, including text and images. By leveraging extensive datasets, these models can align visual and textual representations within a shared embedding space, facilitating applications such as image captioning and text-to-image retrieval. This integrated approach aims to enhance…
Improving image quality and variation in diffusion models without compromising alignment with given conditions, such as class labels or text prompts, is a significant challenge. Current methods often enhance image quality at the expense of diversity, limiting their applicability in various real-world scenarios such as medical diagnosis and autonomous driving, where both high quality and…
Deep neural networks (DNNs) have achieved remarkable success across various fields, including computer vision, natural language processing, and speech recognition. This success is largely attributed to first-order optimizers like stochastic gradient descent with momentum (SGDM) and AdamW. However, these methods face challenges in efficiently training large-scale models. Second-order optimizers, such as K-FAC, Shampoo, AdaBK, and…
Nomic AI has recently unveiled two significant releases in multimodal embedding models: Nomic Embed Vision v1 and Nomic Embed Vision v1.5. These models are designed to provide high-quality, fully replicable vision embeddings that seamlessly integrate with the existing Nomic Embed Text v1 and v1.5 models. This integration creates a unified embedding space that enhances the…