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…
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…