Large Language Models (LLMs) are vital for natural language processing but face inference latency challenges. An innovative approach called Speculative Decoding accelerates this process by allowing multiple tokens to be processed simultaneously, reducing dependency on sequential processing. This method achieves substantial speedups without compromising quality, making real-time, interactive AI applications more practical and broadening LLMs’…

A disgruntled customer of UK parcel delivery company DPD made their customer service chatbot misbehave until the company had to take it down. Musician Ashley Beauchamp got the chatbot to compose a poem about DPD’s poor service and even swear at him. DPD has disabled the AI and is updating it. Beauchamp is still waiting…

The 2024 World Economic Forum in Davos focused on AI, with concerns about AI-driven misinformation and election interference. UN Secretary-General urged collaborative governance to address AI risks, while the European Commission President emphasized AI’s opportunities. Chinese Premier emphasized responsible AI development. Concerns were raised about AI’s impact on election campaigns, with tech companies defending their…

OpenAI partners with Arizona State University to deploy ChatGPT Enterprise, enhancing access to advanced AI capabilities for staff, faculty, and students. Despite initial concerns over AI’s impact, ASU recognizes its potential to aid learning and research. Collaboration with chipmakers underscores the university’s commitment to tech and innovation. The partnership aims to drive advances in tech…

Google DeepMind introduced AlphaGeometry, an AI system excelling in solving geometry Olympiad questions, rivaling human gold medallists. Overcoming limitations in converting human arguments to machine-verifiable formats, AlphaGeometry synthesizes data and utilizes a neural language model and a symbolic deduction engine to solve complex geometry problems. It outperforms previous state-of-the-art geometry theorem provers. [Word count: 69]

The study focuses on the impact of feedback protocols on improving alignment of large language models (LLMs) with human values. It explores the challenges in feedback acquisition, particularly comparing ratings and rankings protocols, and highlights the inconsistency issues. The research emphasizes the significant influence of feedback acquisition on various stages of the alignment pipeline, stressing…

Recent developments in machine translation have led to significant progress, with a focus on reaching near-perfect translations rather than mere adequacy. The introduction of Contrastive Preference Optimization (CPO) marks a major advancement, training models to generate superior translations while rejecting high-quality but imperfect ones. This novel approach has shown remarkable results, setting new standards in…

The University of California researchers developed Group Preference Optimization (GPO), a pioneering approach aligning large language models (LLMs) with diverse user group preferences efficiently. It involves an independent transformer module that adapts the base LLM to predict and align with specific user group preferences, showing superior performance and efficiency over existing strategies. The full paper…

Researchers from ByteDance unveiled the Reinforced Fine-Tuning (ReFT) method to enhance the reasoning skills of LLMs, using math problem-solving as an example. By combining supervised fine-tuning and reinforcement learning, ReFT optimizes learning by exploring multiple reasoning paths, outperforming traditional methods and improving generalization in extensive experiments across different datasets. For more details, refer to the…