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Large language models (LLMS) have been given prominent role in their ability to handle complex reasoning tasks, transformer applications from chatbots to code generation tools. These models are known to take advantage of scaling their calculation during inference, which often produces higher accuracy by dedicating more resources to harsh problems. However, this approach brings significant … Read more

CHALLENGES IN CONSTRUCTION OF EFFECTIVE Prior Data Candes As large language models (LLMS) scale in size and capacity, the choice of prior data remains a critical determinant for downstream performance. Most LLMs are trained on large data sets for web scale such as regular review, which provides broad coverage but lacks explicit domain labels. This … Read more

Despite rapid advances in the vision language modeling, much of the progress of this field is shaped by models trained on proprietary data sets and often depend on distillation from closed source systems. This addiction creates barriers to scientific transparency and reproducibility, especially for tasks involving fine -grained image and video understanding. Benchmark performance may … Read more