Analogical reasoning, which enables understanding relationships between objects, is key to abstract thinking in humans. However, machine learning models often struggle with this task, requiring assistance to draw abstract rules from limited data. A process known as the relational bottleneck has been adopted to help rectify this issue, using attention mechanisms to detect correlations between…
The field of document understanding, which involves transforming documents into meaningful information, has gained significance with the advent of large language models and increasing use of document images across industries. The primary challenge for researchers in this field, however, is the effective extraction of information from documents that contain a mix of text and visual…
The confluence of machine learning (ML) and artificial intelligence (AI) with biomedicine has become essential, especially in the field of digital health. The profusion of high-throughput technologies such as genome-wide sequencing, comprehensive medical image libraries, and large-scale drug perturbation screens reveals extensive and intricate biomedical data. By leveraging advanced ML techniques on this multi-omics data,…
Google’s mobile keyboard app, Gboard, uses statistical decoding to counteract the inherent inaccuracies of touch input on small screens, often referred to as the ‘fat finger’ problem. To assist users, Gboard has several features covering word completion, next-word predictions, active auto-correction and active key correction. However, these models do struggle with more complex errors which…
Large Language Models (LLMs) can sometimes mislead users to make poor decisions by providing wrong information, a phenomenon known as 'hallucination'. To mitigate this, a team of researchers from Stanford University has proposed a new method for linguistic calibration. The new framework involves a two-step training process for LLMs.
In the first stage - supervised finetuning…
Retrieval Augmented Generation (RAG) is a method that aids Large Language Models (LLMs) in producing more accurate and relevant data by incorporating a document retrieval system. Current RAG solutions struggle with multi-aspect queries requiring diverse content from multiple documents. Standard techniques like RAPTOR, Self-RAG, and Chain-of-Note focus on data relevance but are not efficient in…
Human-computer interaction (HCI) is the study of how humans interact with computers, with a specific focus on designing innovative interfaces and technologies. One aspect of HCI that has gained prominence is the integration of large language models (LLMs) like OpenAI's GPT models into educational frameworks, specifically undergraduate programming courses. These AI tools have the potential…
Researchers from Fudan University and Microsoft have developed a novel architecture for language and vision models (LMMs), called "DeepStack." The DeepStack model takes a different approach to processing visual data, thereby improving overall computational efficiency and performance.
Traditional LMMs typically integrate visual and textual data by converting images into visual tokens, which are then processed…