Bias, Toxicity, and Jailbreaking Large Language Models (LLMs)

Understanding Data Labeling What is Data Labeling? Data labeling is the process of adding meaningful tags to raw data like images, text, audio, or video. These tags help machine learning algorithms recognize patterns and make accurate…

Understanding Multi-modal Entity Alignment (MMEA) Multi-modal entity alignment (MMEA) is a method that uses information from different sources to match related entities across various knowledge graphs. By integrating data from text, structure, attributes, and external sources,…

Google Releases Gemma 2 Series Models: Advanced LLM Models in 9B and 27B Sizes Trained on 13T Tokens Practical Solutions and Value Google’s Gemma 2 series introduces two new models, the 27B and 9B, showcasing significant…

“`html Building an Advanced Financial Data Reporting Tool In this tutorial, we will guide you through creating a financial data reporting tool using Google Colab and various Python libraries. You will learn to: Scrape live financial…

Meet StyleMamba: A State Space Model for Efficient Text-Driven Image Style Transfer In a recent study, researchers from Imperial College London and Dell introduced StyleMamba, a framework for transferring picture styles using text prompts to direct…

Understanding Transformer-Based Language Models Transformer-based language models analyze text by looking at word relationships instead of reading in a strict order. They use attention mechanisms to focus on important keywords. However, they struggle with longer texts…

Generative AI is disrupting the creative industry, leading to anxiety and real impacts. Events like the Writers Guild of America strike and layoffs in big companies have highlighted the looming threat. Studies project significant job disruptions,…

Practical Solutions for Enhancing Language Model Safety Preventing Unsafe Outputs Language models can generate harmful content, risking real-world deployment. Techniques like fine-tuning on safe datasets help but are not foolproof. Introducing Backtracking Mechanism The backtracking method…

Mixture-of-experts (MoE) models have transformed AI by dynamically assigning tasks to specialized components. Deployment in low-resource settings presents a challenge due to large size exceeding GPU memory. The University of Washington’s Fiddler optimizes MoE model deployment…

Carnegie Mellon University and Google DeepMind collaborated to develop RoboTool, a system using Large Language Models to enable robots to creatively use tools in tasks with physical constraints and planning. It comprises four components and leverages…

Researchers from McMaster University and FAIR Meta have developed a new machine learning technique called orbital-free density functional theory (OF-DFT) for accurately replicating electronic density in chemical systems. The method utilizes a normalizing flow ansatz to…

Generative AI, particularly large language models (LLMs), has significantly impacted various fields and transformed human-computer interactions. However, challenges arise, leading researchers to introduce SymbolicAI, a neuro-symbolic framework. By enhancing LLMs with domain-invariant solvers and leveraging cognitive…

Recently, a paper on the use of audio-visual synchronization for learning audio-visual representations was accepted at the Machine Learning for Audio Workshop at NeurIPS 2023. The paper discusses the effectiveness of unsupervised training frameworks, particularly the…

Understanding Small Language Models (SLMs) Introduction to SLMs Large language models (LLMs) like GPT-4 and Bard have transformed natural language processing, enabling text generation and problem-solving. However, their high costs and energy consumption limit access for…

Unlocking the Potential of Unstructured Data with Reducto Unstructured data, which makes up about 80% of all company data, including spreadsheets and PDFs, often poses challenges in digital workflows. Reducto, an AI-powered startup, offers a practical…

API-BLEND is a novel dataset that addresses the challenge of integrating APIs into Large Language Models (LLMs) to enhance AI systems. It includes diverse, real-world training data and emphasizes sequencing tasks. Empirical evaluations demonstrate its superiority…