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 …

Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

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 …

While recent language models have the ability to take long contexts as input, relatively little
is known about how well they use longer context. We analyze the performance of language …

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

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 …

As a primary means of information acquisition, information retrieval (IR) systems, such as
search engines, have integrated themselves into our daily lives. These systems also serve …

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 …

As one of the most advanced techniques in AI, Retrieval-Augmented Generation (RAG) can
offer reliable and up-to-date external knowledge, providing huge convenience for numerous …

In this work, we introduce ChatQA, a suite of models that outperform GPT-4 on retrieval-
augmented generation (RAG) and conversational question answering (QA). To enhance …