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 …

Misinformation such as fake news and rumors is a serious threat for information ecosystems
and public trust. The emergence of large language models (LLMs) has great potential to …

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 …

This survey provides an in-depth analysis of knowledge conflicts for large language models
(LLMs), highlighting the complex challenges they encounter when blending contextual and …

Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large
language models (LLMs) due to the intensive knowledge required. Current solutions, like …

We introduce FinanceMath, a novel benchmark designed to evaluate LLMs' capabilities in
solving knowledge-intensive math reasoning problems. Compared to prior works, this study …

Hallucinations pose a significant challenge to the reliability of large language models
(LLMs) in critical domains. Recent benchmarks designed to assess LLM hallucinations …

As large language models (LLMs) increasingly permeate daily lives, there is a growing
demand for real-time interactions that mirror human conversations. Traditional turn-based …

AI Safety is an emerging area of critical importance to the safe adoption and deployment of
AI systems. With the rapid proliferation of AI and especially with the recent advancement of …

While auxiliary information has become a key to enhancing Large Language Models
(LLMs), relatively little is known about how LLMs merge these contexts, specifically contexts …