This concise tutorial, sourced from Anthropic's official GitHub, will guide you on using Claude3 to summarize web page content. Unlike the official tutorial, this one utilizes the model claude-3-5-sonnet-20240620 and uses content from my personal web page as an example to send to the LLM.
More researchers are recognizing the significance of instruction data during the Supervised Fine-Tuning (SFT) stage. In June, I wrote a blog about data generation, but I believe it was somewhat superficial and insufficient. Since then, many new methods have emerged. Therefore, I aim to cover more papers I've read to discuss instruction data generation and selection.
With the rapid development of LLMs, the community requires an efficient and accurate method to automatically evaluate LLM performance, as human annotation is tedious and time-consuming. LLM-as-a-Judge is now an optimized solution for this need.
DSPy is an optimization framework that enhances prompts and responses from models like GPT-4o-mini. It showcases the magic of the framework and demonstrates how to use its powerful optimizers to improve the cost-effective model. The MMLU-Pro dataset is an advanced dataset with complex questions and increased answer choices. The evaluation metric is defined to check if the model's responses match the true answers.
In this short blog, I will test Chameleon, the newest multimodal model from Meta. The baseline models I will choose are GPT-4o, Gemini-1.5-pro, Yi-vision and Yi-Vision-with-TextGrad.
Evaluating LLMs is important for understanding their abilities and solving real business problems. A good evaluation requires sufficient and high-quality data samples, clear judging criteria, meaningful evaluation tasks, and frequent private benchmarks. The process should adapt to the development of LLMs over time.
As the capabilities of Large Language Models (LLMs) continue to evolve, many traditional evaluation benchmarks may require updates. With the rapid progress of these models, researchers are increasingly introducing new evaluation datasets. However, the specific dimensions these datasets assess in the models are often unclear. In this blog, I will explore a series of commonly referenced evaluation datasets and highlight the particular aspects of model capabilities they were designed to assess even though I may not cover all available datasets.
Midjourney provides a platform for exploring different artistic styles and techniques. Whether you're a seasoned artist or a beginner, the tool offers a wide array of options to experiment with and refine your artistic vision. Users can blend various elements, adjust parameters, and see real-time changes, giving them a unique and interactive experience.
DSPy is a framework developed by Stanford. It is used for programming to automatically optimize prompts and weights in Large Language Models (LLMs). DSPy can enhance the reliability of any model, whether it's GPT-4, LLaMA3 or Mistral, for any task you require.
Inspired by Nezhurina et al. 2024, I employ similar questions to evaluate various leading language models, demonstrating their reasoning capabilities. Thus, this blog will resemble a test report. This test is very subjective. So, if the outcome does not meet your expectations, just take it in stride.
TextGrad is an innovative autograd engine, particularly tailored for textual gradients. As a robust framework, it facilitates automatic meticulously implements backpropagation using feedback provided by advanced Large Language Models (LLMs), firmly anchored in the gradient metaphor.
Many studies have shown that large language models can stimulate their ability to follow instructions and generalize on more tasks during the fine-tuning stage. However, if we only rely on manual handwritten instruction data, it will consume a lot of human resources, and the quantity is limited.Therefore, it is essential to explore other automatic methods for generating instruction data.
