Computational biology, an essential field that merges biological research and computer science, has been focusing intently on predicting biomolecular structures. The ability to predict such structures accurately can have immense implications in understanding cellular functions and developing new medical therapies. Despite the complex nature of this discipline, it is instrumental in studying proteins, nucleic acids,…
The researchers from Huazhong University of Science and Technology, the University of New South Wales, and Nanyang Technological University have unveiled a novel framework named HalluVault, aimed at enhancing the efficiency and accuracy of data processing in machine learning and data science fields. The framework is designed to detect Fact-Conflicting Hallucinations (FCH) in Large Language…
Language models play a crucial role in advancing artificial intelligence (AI) technologies, revolutionizing how machines interpret and generate text. As these models grow more intricate, they employ vast data quantities and advanced structures to improve performance and effectiveness. However, the use of such models in large scale applications is challenged by the need to balance…
The challenge of efficiently determining a user's preferences through natural language dialogues, specifically in the context of conversational recommender systems, is a focus of recent research. Traditional methods require users to rate or compare options, but this approach fails when the user is unfamiliar with the majority of potential choices. Solving this problem through Large…
Continual learning (CL), the capability of systems to adapt over time without losing prior knowledge, presents a significant challenge. Neural networks, while adept at processing large amounts of data, can often suffer from catastrophic forgetting, when learning new information may erase what was previously learned. This becomes extremely problematic in scenarios with limited data retention…
Large Language Models (LLMs) have successfully replicated human-like conversational abilities and demonstrated proficiency in coding. However, they continue to grapple with the challenges of maintaining high reliability and stringent abidance to ethical and safety measures. Reinforcement Learning from Human Feedback (RLHF) or Preference-based Reinforcement Learning (PbRL) has emerged as a promising solution to help fine-tune…