Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

Language is essentially a complex, intricate system of human expressions governed by
grammatical rules. It poses a significant challenge to develop capable AI algorithms for …

To mitigate the high inference latency stemming from autoregressive decoding in Large
Language Models (LLMs), Speculative Decoding has emerged as a novel decoding …

Abstract We propose Text2Motion, a language-based planning framework enabling robots
to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural …

We propose an efficient method to ground pretrained text-only language models to the
visual domain, enabling them to process arbitrarily interleaved image-and-text data, and …

Abstract Large Vision-Language Models (LVLMs) have advanced considerably intertwining
visual recognition and language understanding to generate content that is not only coherent …

Retrieval-augmented in-context learning has emerged as a powerful approach for
addressing knowledge-intensive tasks using frozen language models (LM) and retrieval …

This survey addresses the crucial issue of factuality in Large Language Models (LLMs). As
LLMs find applications across diverse domains, the reliability and accuracy of their outputs …

Language models (LMs) often struggle to pay enough attention to the input context, and
generate texts that are unfaithful or contain hallucinations. To mitigate this issue, we present …

We present CM3Leon (pronounced “Chameleon”), a retrieval-augmented, tokenbased,
decoder-only multi-modal language model capable of generating and infilling both text and …