The field of large language models (LLMs) is moving towards improving reliability and trustworthiness. Recent developments focus on addressing hallucination and factuality deficits, which are major obstacles to the widespread adoption of LLMs. Researchers are exploring innovative approaches to reinforce factual accuracy and precision, such as integrating knowledge consistency, attention-level knowledge integration, and incentive-aligned frameworks. These advancements have the potential to significantly improve the reliability of LLMs in various applications, including long-form generation and summarization. Noteworthy papers in this area include: Knowledge-Level Consistency Reinforcement Learning Framework, which introduces a novel framework to improve factual recall and precision. Fact Grounded Attention, which eliminates hallucination in LLMs by injecting verifiable knowledge into the attention mechanism. TruthRL, which presents a general reinforcement learning framework to directly optimize the truthfulness of LLMs. Adaptive Planning for Multi-Attribute Controllable Summarization, which proposes a training-free framework for controllable summarization. Trustworthy Summarization via Uncertainty Quantification and Risk Awareness, which integrates uncertainty quantification and risk-aware mechanisms to improve the reliability of automatic summarization.