Have you ever wondered how AI tools like ChatGPT, powered by large language models (LLMs), manage to answer nearly any question posed by users, especially in open-domain queries that require extensive knowledge or up-to-date facts? Relying solely on traditional LLMs to generate answers can be incredibly challenging. Here’s why: 1. Knowledge Limitations: LLMs are trained […]
Traditional Retrieval-Augmented Generation (RAG) systems often struggle when it comes to handling global queries that require summarizing entire datasets. To address this limitation, a team from Microsoft Research and associated departments has developed a novel method called Graph RAG. This approach combines the strengths of graph-based indexing and query-focused summarization to enhance the ability of
In traditional Transformer architectures, the computational complexity grows quadratically (O(L²)) with the length of the sequence, making it resource-intensive to process long sequences. This high demand for resources makes it impractical to extend context length directly. Even when fine-tuned on longer sequences, LLMs often struggle with extrapolation, failing to perform well on sequences longer than
Scaling LLMs for Long Texts with FocusLLM Read More »
Paper Skimming
Over the past year, the explosion of generative AI has flooded the world with AI-generated content. As we move forward, training future models on this machine-generated data seems inevitable. A recent study explores the potential issues when large language models are trained using data generated by other models. The researchers found that as training progresses
Averting Model Collapse: The Importance of Human Data in AI Training Read More »
Research Highlights
As generative AI took the spotlight last year, 2024 is emerging as the year of large-scale adoption. While some fear job displacement, others are already using AI tools to enhance productivity. Today’s AI excels as an assistant, but it’s still far from replacing human innovation, complex reasoning, and interdisciplinary integration. According to a recent McKinsey
Maximizing AI Potential: From Adoption to Organizational Transformation Read More »
Thoughts and Reflections
As Large Language Models (LLMs) continue to evolve, their increasing complexity has brought a new set of challenges. One significant issue is the generation of outputs that are often vague, ambiguous, or logically inconsistent. These issues make it difficult for users to interpret and trust the AI’s reasoning. In response, OpenAI has introduced a novel
Enhancing AI Output: Understanding Prover-Verifier Games Read More »
Research Highlights
I’m thrilled about the potential of Meta’s SAM 2 model in revolutionizing medical and biological research. This advanced segmentation model could significantly enhance: • Medical Imaging: Automating the segmentation of organs and anomalies in CT and MRI scans, leading to quicker, more accurate diagnoses.• Cell and Tissue Analysis: Precise segmentation of cells and tissues in
Revolutionizing Healthcare and Research with Meta’s SAM 2 Model Read More »
Posts, Thoughts and Reflections
In the past few weeks, both OpenAI and Google have introduced smaller-scale large-language models. OpenAI’s ChatGPT-4o Mini boasts approximately 8B parameters, while Google’s Gemma 2 2B is even more compact at just 2B parameters—small enough to run on the free tier of T4 GPUs in Google Colab. It’s exciting to see the advancement towards more
Advances in Lightweight LLMs and On-Device AI for Enhanced Privacy Read More »
Posts, Thoughts and ReflectionsThis summer has been a whirlwind of exciting AI developments. We’ve seen the launch of comprehensive language models, the evolution of segment-anything technology, privacy-focused solutions from Apple, and the rise of edge models—all bringing immense application potential. Huge appreciation to Meta AI for their commitment to open-source. It’s empowering us, as AI practitioners, to explore,
The Llama 3 Herd of Models Read More »
Posts
Reinforcement Learning from Human Feedback (RLHF) has been fundamental to training models like ChatGPT. However, as models advance, the quality of human feedback reaches a limit, hindering further improvements. OpenAI’s researchers have tackled this challenge by developing CriticGPT—a model trained to detect and correct subtle errors in AI-generated outputs. 🚀 How It Works: 1. Tampering:
Enhancing AI with Human-Like Precision: The CriticGPT Approach Read More »
Paper Skimming