Artificial intelligence (AI) alignment strategies, such as Direct Preference Optimization (DPO) and Reinforcement Learning with Human Feedback (RLHF) combined with supervised fine-tuning (SFT), are essential for the safety of Large Language Models (LLMs). They work to modify these AI models to reduce the chance of hazardous interactions. However, recent research has uncovered significant weaknesses in…
University of Waterloo researchers have introduced GenAI-Arena, a user-centric evaluation platform for generative AI models, filling a critical gap in fair and efficient automatic assessment methods. Traditional metrics like FID, CLIP, FVD provide insights into visual content generation but may not sufficiently evaluate user satisfaction and aesthetic qualities of generated outputs. GenAI-Arena allows users not…
Large Generative Models (LGMs) such as GPT, Stable Diffusion, Sora, and Suno have significantly advanced content creation, improving the effectiveness of real-world applications. Unlike preceding models that trained on small, specialized datasets, LGMs gain their success from extensive training on broad, well-curated data from various sectors. This leads to a question: Can we create large…
CodeParrot AI, a startup offering AI-powered tools, aims to make the coding process more manageable for designers and developers. Its main function is to simplify the building of web parts by transforming Figma design into code components for React, Vue, and Angular. This streamlining tool automates the frontend work, ultimately making coding more efficient and…
Large language models (LLMs) like GPT-3 require substantial computational resources for their deployment, making it challenging to use them on resource-constrained devices. Strategies to boost the efficiency of LLMs like pruning, quantization, and attention optimization have been developed, but these can often lead to decreased accuracy or continue to rely heavily on energy-consuming multiplication operations.…
Large Language Models (LLMs) are complex artificial intelligence tools capable of amazing feats in natural language processing. However, these large models require extensive fine-tuning to adapt to specific tasks, a process that usually involves adjusting a considerable number of parameters and consequently consuming significant computational resources and memory. This means the fine-tuning of LLMs is…