Machine learning

Multimodal large language models (MLLMs) are crucial tools for combining the capabilities of natural language processing (NLP) and computer vision, which are needed to analyze visual and textual data. Particularly useful for interpreting complex charts in scientific, financial, and other documents, the prime challenge lies in improving these models to understand and interpret charts accurately.…

The field of software engineering has made significant strides with the development of Large Language Models (LLMs). These models are trained on comprehensive datasets, allowing them to efficiently perform a myriad of tasks which comprise of code generation, translation, and optimization. LLMs are increasingly being employed for compiler optimization. However, traditional code optimization methods require…

Large Language Models (LLMs) have played a notable role in enhancing the understanding and generation of natural language. They have, however, faced challenges in processing long contexts due to restrictions in context window size and memory usage. This has spawned research to address these limitations and come up with ways of making the LLMs work…

Large language models (LLMs) have made significant progress in the understanding and generation of natural language, but their application over long contexts is still limited due to constraints in context window sizes and memory usage. It's a pressing concern as the demand for LLMs' ability to handle complex and lengthy tasks is on the rise. Various…