Retrieval-Augmented Generation (RAG) has significantly enhanced the capabilities of large language models (LLMs) by incorporating external knowledge to provide more contextually relevant and accurate responses. However, this technique comes with a major downside: it often leads to high computational and memory costs. These challenges are primarily due to the injection of long sequences of external… →
Large Language Models find it challenging to understand Mathematical reasoning. Mathematical reasoning involves various cognitive tasks like understanding and manipulating mathematical concepts, solving problems, and making logical deductions. Existing methods in this domain have been established to enhance the mathematical ability of LLMs. However, few recognize the value of state transition for LLM reasoning, which… →
Large language models (LLMs) have become foundational in natural language processing, especially in applications where understanding complex text data is critical. These models require vast amounts of computational resources due to their size, posing latency, memory usage, and power consumption challenges. To make LLMs more accessible for scalable applications, researchers have been developing techniques to… →
Delays or errors in diagnosing pneumoperitoneum, with air outside the intestines within the peritoneal cavity, can severely impact patient survival and health outcomes. In adults, most cases result from a perforated viscus, with up to 90% needing surgical intervention. While CT scans are the preferred diagnostic tool for their high accuracy, interpretation delays are common… →
Retrieval-augmented generation (RAG) has been shown to improve knowledge capabilities and reduce the hallucination problem of LLMs. The Web is a major source of external knowledge used in RAG and many commercial systems such as ChatGPT. However, current RAG implementations face a fundamental challenge in their knowledge-processing approach. The conventional method of converting HTML documents… →
In today’s world, Graph similarity computation (GSC) plays an important role in various applications such as code detection, molecular graph similarity, image matching, etc., by evaluating the similarity between two graphs, and it is based on Graph similarity learning. Graph Edit Distance (GED) and Maximum Common Subgraph (MCS) are widely used to measure graph similarity.… →
Document Visual Question Answering (DocVQA) represents a rapidly advancing field aimed at improving AI’s ability to interpret, analyze, and respond to questions based on complex documents that integrate text, images, tables, and other visual elements. This capability is increasingly valuable in finance, healthcare, and law settings, as it can streamline and support decision-making processes that… →
In recent years, Automatic Speech Recognition (ASR) technology has gained significant traction, transforming industries ranging from healthcare to customer support. However, achieving accurate transcription across diverse languages, accents, and noisy environments remains challenging. Current speech-to-text models often face issues like inaccuracies in understanding complex accents, handling domain-specific terminology, and dealing with background noise. The need… →
Adam is widely used in deep learning as an adaptive optimization algorithm, but it struggles with convergence unless the hyperparameter β2 is adjusted based on the specific problem. Attempts to fix this, like AMSGrad, require the impractical assumption of uniformly bounded gradient noise, which doesn’t hold in cases with Gaussian noise, as seen in variational… →
In an exciting update for developers, Google has launched Gemini, a new AI model that promises to be more accessible and developer-friendly. Gemini, designed to rival models like OpenAI’s GPT-4, has been made easier to access and integrate into various applications, thanks to Google’s recent initiatives. If you’re a developer exploring powerful alternatives or complementary… →