4 accepted papers
Large language models (LLMs) often generate hallucinations---unsupported content that undermines reliability. While most prior works frame hallucination detection as a binary task, many real-world applications require identifying hallucinated spans, which is a multi-step decision making process. Thi…
Vision Language Models (VLMs) like LLaVA encode images into tokens aligned to the word embedding space of the LLM decoder. Scaling input image resolution is essential for improving performance, especially in text-rich image understanding tasks. However, popular visual encoders such as CLIP-pretraine…
Large language models (LLMs) are commonly trained on datasets consisting of fixed-length token sequences. These datasets are created by randomly concatenating documents of various lengths and then chunking them into sequences of a predetermined target length (concat-and-chunk). Recent attention impl…
Much current study of legged locomotion has rightly focused on foot traction forces, including on granular media. Future legged millirobots will need to go through terrain, such as brush or other vegetation, where the body contact forces significantly affect locomotion. In this work, a (previously d…