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arXiv:2408.07981v1 Announce Type: new Abstract: Multimodal large language models (LLMs) have achieved notable success across various domains, while research in the medical field has largely focused on unimodal images. Meanwhile, current general-domain multimodal models for videos still lack the capabilities to understand and engage in conversations about surgical videos. One major contributing factor is the absence of datasets in the surgical field. In this paper, we create a new dataset, Surg-QA, consisting of 102,000 surgical video-instruction pairs, the largest of its kind so far. To build such a dataset, we propose a novel two-stage question-answer generation pipeline with LLM to…

arXiv:2408.08072v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved significant advancements, however, the common learning paradigm treats LLMs as passive information repositories, neglecting their potential for active learning and alignment. Some approaches train LLMs using their own generated synthetic data, exploring the possibility of active alignment. However, there is still a huge gap between these one-time alignment methods and the continuous automatic alignment of humans. In this paper, we introduce textbf{I-SHEEP}, an textbf{I}terative textbf{S}elf-Entextbf{H}anctextbf{E}mtextbf{E}nt textbf{P}aradigm.This human-like paradigm enables LLMs to textbf{continuously self-align from scratch with nothing}. Compared to the one-time alignment method Dromedary cite{sun2023principledriven}, which refers to the…