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 …

Large language models (LLMs) have emerged as a widely-used tool for information
seeking, but their generated outputs are prone to hallucination. In this work, our aim is to …

Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the
hallucination of large language models (LLMs). However, existing research lacks rigorous …

Prompting-based large language models (LLMs) are surprisingly powerful at generating
natural language reasoning steps or Chains-of-Thoughts (CoT) for multi-step question …

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 …

The research field of Information Retrieval (IR) has evolved significantly, expanding beyond
traditional search to meet diverse user information needs. Recently, Large Language …

While Transformers have shown remarkable success in natural language processing, their
attention mechanism's large memory requirements have limited their ability to handle longer …

The recent burst in Large Language Models has opened new frontiers in human-like text
processing and generation. However, alongside their remarkable growth, Large Language …

Abstract Large Language Models (LLMs) have shown promising performance in knowledge-
intensive reasoning tasks that require a compound understanding of knowledge. However …