Monte Carlo (MC) methods rely on repeated random sampling, so they are widely utilized for simulating and approximating complicated real-world systems. These techniques work especially well for financial mathematics, numerical integration, and optimization issues, particularly those about risk and derivative pricing. However, for complex issues in Monte Carlo, an unfeasibly large number of samples are…
Chain-of-Thought (CoT) reasoning enhances the capabilities of LLMs, allowing them to perform more complex reasoning tasks. Despite being primarily trained for next-token prediction, LLMs can generate detailed steps in their responses when prompted to explain their thought process. This ability, which resembles logical reasoning, is paradoxical since LLMs are not explicitly designed for reasoning. Studies…
Zyphra announced the release of Zyda, a groundbreaking 1.3 trillion-token open dataset for language modeling. This innovative dataset is set to redefine the standards of language model training and research, offering an unparalleled combination of size, quality, and accessibility. Zyda amalgamates several high-quality open datasets, refining them through rigorous filtering and deduplication. The result is…
Large language models (LLMs) have revolutionized code generation, but their autoregressive nature poses a significant challenge. These models generate code token by token, without access to the program’s runtime output from the previously generated tokens. This lack of a feedback loop, where the model can observe the program’s output and adjust accordingly, makes it difficult…
Language models (LMs) are designed to reflect a broad range of voices, leading to outputs that don’t perfectly match any single perspective. To avoid generic responses, one can use LLMs through supervised fine-tuning (SFT) or reinforcement learning with human feedback (RLHF). However, these methods need huge datasets, making them impractical for new and specific tasks.…
The Qwen Team recently unveiled their latest breakthrough, the Qwen2-72B. This state-of-the-art language model showcases advancements in size, performance, and versatility. Let’s look into the key features, performance metrics, and potential impact of Qwen2-72B on various AI applications. Qwen2-72B is part of the Qwen2 series, which includes a range of large language models (LLMs) with…
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…