The use of large language models (LLMs), such as ChatGPT, has significantly increased in academic writing, resulting in observable shifts in writing style and vocabulary, particularly in biomedical research. Concerns have risen around the authenticity and originality of scientific content and its implications for research integrity and the evaluation of academic contributions.
Traditional methods for detecting…
Retrieval-Augmented Generation (RAG) methods improve the ability of large language models (LLMs) by incorporating external knowledge gleaned from vast data sets. These methods are particularly useful for open-domain question answering where detailed and accurate answers are needed. RAG systems can utilize external information to complement the inherent knowledge built into LLMs, making them more effective…
Large language models (LLMs) and latent variable models (LVMs) can present significant challenges during deployment, such as balancing low inference overhead and the rapid change of adapters. Traditional methods, such as Low Rank Adaptation (LoRA), often result in increased latency or loss of rapid switching capabilities. This can prove particularly problematic in resource-constrained settings like…
Large Language Models (LLMs) are an essential development in the field of Natural Language Processing (NLP), capable of understanding, interpreting, and generating human language. Despite their abilities, improving these models to follow detailed instructions accurately remains a challenge, which is crucial as precision is instrumental in applications ranging from customer service bots to complex AI…
Large language models (LLMs) are crucial in the field of natural language processing (NLP). However, their performance in tasks requiring visual and spatial reasoning is generally poor. Researchers from Columbia University have proposed a new approach to tackle this issue. Their method, called Whiteboard-of-Thought (WoT) prompting, aims to enhance the visual reasoning abilities of multimodal…
Artificial Intelligence has significant potential to revolutionize healthcare by predicting disease progression using extensive health records, enabling personalized care. Multi-morbidity, the presence of multiple acute and chronic conditions in a patient, is an important factor in personalized healthcare. Traditional prediction algorithms often focus on specific diseases, but there is a need for comprehensive models that…
Artificial Intelligence (AI) models have huge potential to predict disease progression through analysis of health records, facilitating a more personalised healthcare service. This predictive capability is crucial in enabling more proactive health management of patients with chronic or acute illnesses related to lifestyle, genetics and socio-economic factors. Despite the existence of various predictive algorithms for…
Large Language Models (LLMs), significant advancements in the field of artificial intelligence (AI), have been identified as potential carriers of harmful information due to their extensive and varied training data. This information can include instructions on creating biological pathogens, which pose a threat if not adequately managed. Despite efforts to eliminate such details, LLMs can…
Language models (LMs) are a vital component of complex natural language processing (NLP) tasks. However, optimizing these models can be a tedious and manual process, hence the need for automation. Various methods to optimize these programs exist, but they often fall short, especially when handling multi-stage LMs that have diverse architectures.
A group of researchers…
Materials science is a field of study that focuses on understanding the properties and performance of various materials, with an emphasis on innovation and the creation of new material for a range of applications. Particular challenges in this field involve integrating large amounts of visual and textual data from scientific literature to enhance material analysis…
Materials science focuses on the study of materials to develop new technologies and improve existing ones. Most researchers in this realm use scientific principles such as physics, chemistry, and understanding of engineering. One major challenge in materials science is collating visual and textual data for analysis to improve material inventions. Traditional methods rarely combine both…