Large language models (LLMs) demonstrate powerful capabilities, but they still face
challenges in practical applications, such as hallucinations, slow knowledge updates, and …

As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Abstract Retrieval-Augmented Language Modeling (RALM) methods, which condition a
language model (LM) on relevant documents from a grounding corpus during generation …

Responsible innovation on large-scale Language Models (LMs) requires foresight into and
in-depth understanding of the risks these models may pose. This paper develops a …

This survey reviews works in which language models (LMs) are augmented with reasoning
skills and the ability to use tools. The former is defined as decomposing a potentially …

Large language models (LLMs) have shown promise in proving formal theorems using proof
assistants such as Lean. However, existing methods are difficult to reproduce or build on …

Even the largest neural networks make errors, and once-correct predictions can become
invalid as the world changes. Model editors make local updates to the behavior of base (pre …

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family of
Transformer-based neural language models specialized for dialog, which have up to 137B …

Abstract Contrastive Vision-Language Pre-training, known as CLIP, has provided a new
paradigm for learning visual representations using large-scale image-text pairs. It shows …

This survey reviews text-to-image diffusion models in the context that diffusion models have
emerged to be popular for a wide range of generative tasks. As a self-contained work, this …