Inspired by multi-modal models that align different modalities with natural language, the text has recently been adopted to provide a unified feature space for diverse graphs.

However, as the number of serving tokens increases, the key-value (KV) caches occupy a lot of GPU memory, rendering the inference of large language models memory-bounded [29].

Atypical MLLM [7]consists of a pre-trained LLM, a pre-trained modality encoder, and a modality interface to connect them.

To mitigate this gap, we first refine and extend the definition of honesty inLLMs based onthedefinition proposed byAskelletal.[14],astheability

We introduce quantization with incoherence processing (QuIP), a new method based on the insight that quantization benefits from incoherent weight and Hessian matrices, i.e., from the weights being even in magnitude and the

We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos.