What is in your LLM-based framework?To maintain high standards in clarity and reproducibility, authors need to clearly mention and describe the use of GPT-4 and other large language models in their work.doi:10.1038/s42256-024-00896-6Nature Publishing Group UKNature Machine Intelligence...
AI agents built on large language models control the path to solving a complex problem. They can typically act on feedback to refine their plan of action, a capability that can improve performance and help them accomplish more sophisticated tasks. 📝 Learn more about what IBM is doing with ...
Why should you fine-tune an LLM? Cost benefits Compared to prompting, fine-tuning is often far more effective and efficient for steering an LLM’s behavior. By training the model on a set of examples, you’re able to shorten your well-crafted prompt and save precious input tokens without ...
TheLLMOps platformis a collaborative environmentwhere the complete operational and monitoring tasks of the LLM lifecycle are automated. These platforms allow fine-tuning, versioning, and deployment in a single space. Additionally, these platforms offer varied levels of flexibility based on whether one c...
is and what it can do (and how scared we should be). They matter when this technology is being built into software we use every day, from search engines to word-processing apps to assistants on your phone. AI is not going away. But if we don’t know what we’re being sold, who...
Marketers can train an LLM toorganize customer feedback and requests into clusters or segment products into categories based on product descriptions. Large language models are still in their early days, and their promise is enormous; a single model with zero-shot learning capabilities can solve near...
curated with GPT-4. To our knowledge, this work is the first to enable MLLMs to create free-form interleaved content with a learning synergy on both sides. As a foundational learning framework, DREAMLLM is adaptable across all modalities, laying a promising foundation for future multimodal learn...
Retrieval-augmented generation (RAG) is an AI framework that retrieves data from external sources of knowledge to improve the quality of responses. This natural language processing (NLP) technique is commonly used to make large language models (LLMs) more accurate and up to date. ...
introduces the newFilesview for repository-wide visibility, a Roslyn syntax tree visualizer, and numerous enhancements for debugging both .NET and C++ solutions. Game developers can leverage the Unity Profiler integration, while AI power users benefit from a fresh selection of newly supported LLMs. ...
introduces the newFilesview for repository-wide visibility, a Roslyn syntax tree visualizer, and numerous enhancements for debugging both .NET and C++ solutions. Game developers can leverage the Unity Profiler integration, while AI power users benefit from a fresh selection of newly supported LLMs. ...