Google AI researchers have developed a new Transformer network dubbed TransformerFAM, aimed to enhance performance in handling extremely long context tasks. Despite Transformers proving revolutionary in the domain of deep learning, they have limitations due to their quadratic attention complexity— an aspect that curtails their ability to process infinitely long inputs. Existing Transformers often forget…
The increasing demand for AI-generated content following the development of innovative generative Artificial Intelligence models like ChatGPT, GEMINI, and BARD has amplified the need for high-quality text-to-audio, text-to-image, and text-to-video models. Recently, supervised fine-tuning-based direct preference optimisation (DPO) has become a prevalent alternative to traditional reinforcement learning methods in lining up Large Language Model (LLM)…
Tango 2: The Emerging Frontier in Text-to-Audio Synthesis and Its Outstanding Performance Indicators
As demand for AI-generated content continues to increase, particularly in the multimedia realm, the need for high-quality, quick production models for text-to-audio, text-to-image, and text-to-video conversions has never been greater. An emphasis is placed on enhancing the realistic nature of these models in regard to their input prompts.
A novel approach to adjust Large Language Model…
Digital media has ushered in the requirement for precision in the generation and control of images and videos. This need led to the development of systems like ControlNets, which allow explicit manipulation of visual content using various conditions such as depth maps, canny edges, and human poses. Nonetheless, integration of these technologies with new models…
The transition of Reinforcement Learning (RL) from theory to real-world application has been hampered by sample inefficiency, especially in risky exploration environments. The challenges include a distribution shift between the target policy and the collected data, resulting in overestimation bias and an overly optimistic target policy. A new method proposed by researchers from Oxford University,…
Language model-based machine learning systems, or LLMs, are reaching beyond their previous role in dialogue systems and are now actively participating in real-world applications. There is an increasing belief that many web interactions will be facilitated by systems driven by these LLMs. However, due to the complexities involved, humans are presently needed to verify the…
Large Language Models (LLMs) like those used in Microsoft Bing or Google Search are capable of providing natural language responses to user queries. Traditional search engines often struggle to provide cohesive responses, only offering relevant page results. LLMs improve upon this by compiling results into understandable answers. Yet, issues arise with keeping LLMs current with…