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 …

An Artificial Intelligence (AI) agent is a software entity that autonomously performs tasks or
makes decisions based on pre-defined objectives and data inputs. AI agents, capable of …

While large language models (LLMs) have demonstrated remarkable capabilities across a
range of downstream tasks, a significant concern revolves around their propensity to exhibit …

Training large language models (LLMs) with open-domain instruction following data brings
colossal success. However, manually creating such instruction data is very time-consuming …

Evaluating the factuality of long-form text generated by large language models (LMs) is non-
trivial because (1) generations often contain a mixture of supported and unsupported pieces …

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 …

Training large language models (LLMs) with open-domain instruction following data brings
colossal success. However, manually creating such instruction data is very time-consuming …

Abstract Language models (LMs) often exhibit undesirable text generation behaviors,
including generating false, toxic, or irrelevant outputs. Reinforcement learning from human …

By providing external information to large language models (LLMs), tool augmentation
(including retrieval augmentation) has emerged as a promising solution for addressing the …

Evaluating retrieval-augmented generation (RAG) systems traditionally relies on hand
annotations for input queries, passages to retrieve, and responses to generate. We …