LLM2LLM: UC Berkeley, ICSI and LBNL Researchers’ Innovative Approach to Boosting Large Language Model Performance in Low-Data Regimes with Synthetic Data

The constantly evolving field of Artificial Intelligence emphasizes the need for expertise in Large Language Model (LLM) application development and Retrieval Augmented Generation (RAG) workflows. Monster API offers a user-friendly platform for fine-tuning and deploying open-source…

The Value of AI in Software Development Practical Solutions and Challenges The potential of AI to automate and assist in coding can transform software development, making it faster and more efficient. However, ensuring the production of…

Recent research has focused on artificial multimodal representation learning, particularly in the integration of tactile perception. Touch-vision-language (TVL) dataset and benchmark have been introduced by UC Berkeley, Meta AI, and TU Dresden, aiming to advance touch…

Researchers from UC Berkeley, Toyota Technological Institute at Chicago, ShanghaiTech University, and other institutions propose a new deep network design called CRATE, which stands for “coding-rate” transformer. CRATE aims to bridge the gap between theory and…

Transforming Natural Language Processing with SmolLM2 Recent advancements in large language models (LLMs) like GPT-4 and Meta’s LLaMA have changed how we handle natural language tasks. However, these large models have some drawbacks, especially regarding their…

NVIDIA has introduced the HELPSTEER dataset, a collection of annotated responses that influence helpfulness in language models. The dataset covers qualities such as accuracy, coherence, complexity, verbosity, and overall helpfulness. Researchers used the dataset to train…

The text is about the new updates in Python SDK, AI-assisted labeling, and a growing library of generative models.

Microsoft researchers have unveiled CodeOcean, a new method to improve instruction data quality for fine-tuned models. The approach involves categorizing instruction data into four code-related tasks and using WaveCoder models for tuning. This enhances the generalization…

Challenges with Implicit Graph Neural Networks (IGNNs) The main issues with IGNNs are their slow inference speed and limited scalability. Although they effectively manage long-range dependencies in graphs, they rely on complex fixed-point iterations that are…

Practical Solutions for Safe AI Language Models Challenges in Language Model Safety Large Language Models (LLMs) can generate offensive or harmful content due to their training process. Researchers are working on methods to maintain language generation…

Researchers have developed a new framework using sparse autoencoders to make neural network models more understandable. The framework identifies interpretable features within the models, addressing the challenge of interpretability at the individual neuron level. The researchers…

AI models like GPT-4, used by companies such as OpenAI and Meta, can infer personal information from our online chats and comments, even when we think we’re not revealing anything personal. Researchers found that GPT-4 could…

Fine-Tuning Llama-2 7B Chat for Python Code Generation Overview In this tutorial, we will show you how to fine-tune the Llama-2 7B Chat model for generating Python code. We will use techniques like **QLoRA**, **gradient checkpointing**,…

Scientists from Bar-Ilan University explore the necessity of deep learning in AI and propose alternative machine learning techniques for intricate classification tasks, while continuing their studies on tree-like architectures.

Evaluating AI in Medical Tasks Understanding Limitations of Traditional Benchmarks Traditionally, large language models (LLMs) in medicine have been evaluated using multiple-choice questions. However, these tests often don’t reflect real clinical situations and can lead to…

The University of Hong Kong, XLang Lab, Salesforce Research, Sea AI Lab, University of Washington, and MIT CSAIL have developed Lemur and Lemur-Chat, two state-of-the-art models for language agents. By combining natural language and coding abilities,…