With advancements in model architectures and training methods, Large Language Models (LLMs) such as OpenAI's GPT-3 have showcased impressive capabilities in handling complex question-answering tasks. However, these complex responses can also lead to hallucinations, where the model generates plausible but incorrect information. This is also compounded by the fact that these LLMs generate responses word-by-word,…
Large Language Models (LLMs) have gained significant traction in various applications but they need robust safety measures for responsible user interactions. Current moderation solutions often lack detailed harm type predictions or customizable harm filtering. Now, researchers from Google have introduced ShieldGemma, a suite of content moderation models ranging from 2 billion to 27 billion parameters,…
Large Language Models (LLMs) have transformed natural language processing, demonstrating impressive performance across an assortment of tasks. The Scaling Law suggests that increased model size enhances LLMs' capability to comprehend context and handle long sequences. Applications such as document summarization, code generation, and conversational AI leverage these properties. However, the increased cost and efficiency associated…
Apple's researchers have risen to the challenge of developing AI language models that prioritize efficiency, accuracy, ethical considerations, and user privacy. Two such models have been developed: one with three billion parameters that is optimized for on-device use, and a larger server-based model made for Apple's Private Cloud Compute. These models take us closer to…
Direct Preference Optimization (DPO) is a sophisticated training technique used for refining large language models (LLMs). It does not depend on a single gold reference like traditional supervised fine-tuning, instead, it trains models to identify quality differences among multiple outputs. Adding reinforcement learning approaches, DPO can learn from feedback, making it a useful technique for…
The rapid development of Large Language Models (LLMs) has transformed multiple areas including generative AI, Natural Language Understanding, and Natural Language Processing. However, hardware constraints have often limited the ability to run these models on devices such as laptops, desktops, or mobiles. In response to this, the PyTorch team has developed Torchchat, a versatile framework…
Google's AI research team, DeepMind, has unveiled Gemma 2 2B, its new, sophisticated language model. This version, supporting 2.6 billion parameters, is optimized for on-device use and is a top choice for applications demanding high performance and efficiency. It holds enhancements for handling massive text generation tasks with more precision and higher levels of efficiency…
Researchers from Baidu Inc., China, have unveiled a self-reasoning framework that greatly improves the reliability and traceability of Retrieval-Augmented Language Models (RALMs). RALMs augment language models with external knowledge, decreasing factual inaccuracies. However, they face reliability and traceability issues, as noisy retrieval may lead to incorrect responses, and a lack of citations makes verifying these…
A recent research paper by the University of Washington and Allen Institute for AI researchers has examined the use of abstention in large language models (LLMs), emphasizing its potential to minimize false results and enhance the safety of AI. The study investigates the current methods of abstention incorporated during the different development stages of LLMs…