Recent methodologies in LLM self-training mostly rely on LLM generating responses and
filtering those with correct output answers as training data. This approach often yields a low …

Background It is unknown whether large language models (LLMs) may facilitate time-and
resource-intensive text-related processes in evidence appraisal. Objectives To quantify the …

Large language models (LLMs) and vision-language models (VLMs) have demonstrated
remarkable performance across a wide range of tasks and domains. Despite this promise …

Data science and engineering workflows often span multiple stages, from warehousing to
orchestration, using tools like BigQuery, dbt, and Airbyte. As vision language models (VLMs) …

Tensor Attention, a multi-view attention that is able to capture high-order correlations among
multiple modalities, can overcome the representational limitations of classical matrix …

Large Language Models (LLMs) have demonstrated remarkable capabilities across various
fields, from natural language understanding to text generation. Compared to non-generative …

Large language models (LLMs) excel at a wide range of tasks, but choosing the right model
often involves balancing performance and cost. Powerful models offer better results but are …

The self-attention mechanism is the key to the success of transformers in recent Large
Language Models (LLMs). However, the quadratic computational cost $ O (n^ 2) $ in the …

Safety alignment is crucial to ensure that large language models (LLMs) behave in ways that
align with human preferences and prevent harmful actions during inference. However …

Recent research demonstrates that the nascent fine-tuning-as-a-service business model
exposes serious safety concerns--fine-tuning over a few harmful data uploaded by the users …