Although large language models (LLMs) excel in knowledge recall and reasoning, their static nature leads to outdated information as the real world evolves or when adapting to domain-specific knowledge, highlighting the need for effective knowledge injection. However, current research on knowledge injection remains superficial, mainly focusing on knowledge memorization and retrieval. This paper proposes a four-tier knowledge injection framework that systematically defines the levels of knowledge injection: memorization, retrieval, reasoning, and association. Based on this framework, we introduce DeepKnowledge, a synthetic experimental testbed designed for fine-grained evaluation of the depth of knowledge injection across three knowledge types (novel, incremental, and updated). We then explore various knowledge injection scenarios and evaluate the depth of knowledge injection for each scenario on the benchmark. Experimental results reveal key factors to reach each level of knowledge injection for LLMs and establish a mapping between the levels of knowledge injection and the corresponding suitable injection methods, aiming to provide a comprehensive approach for efficient knowledge injection across various levels. The code is available at [https://github.com/icip-cas/Knowledge-Learning-Toolkits](https://github.com/icip-cas/Knowledge-Learning-Toolkits).

“Understanding documents is central to many real-world tasks but remains a challenging topic.Unfortunately, there is no well-established consensus on how to comprehensively evaluate docu-ment understanding abilities, which significantly hinders the fair comparison and measuring theprogress of the field. To benchmark document understanding researches, this paper summarizesfour representative abilities, i.e., document classification, document structural analysis, docu-ment information extraction, and document transcription. Under the new evaluation framework,we propose Document Language Understanding Evaluation – DLUE, a new task suite whichcovers a wide-range of tasks in various forms, domains and document genres. We also systemat-ically evaluate six well-established transformer models and representative LLMs on DLUE, andfind that due to the lengthy content, complicated underlying structure and dispersed knowledge,document understanding is still far from being solved in complex real-world scenarios.”

Events are considered as the fundamental building blocks of the world. Mining event-centric opinions can benefit decision making, people communication, and social good. Unfortunately, there is little literature addressing event-centric opinion mining, although which significantly diverges from the well-studied entity-centric opinion mining in connotation, structure, and expression. In this paper, we propose and formulate the task of event-centric opinion mining based on event-argument structure and expression categorizing theory. We also benchmark this task by constructing a pioneer corpus and designing a two-step benchmark framework. Experiment results show that event-centric opinion mining is feasible and challenging, and the proposed task, dataset, and baselines are beneficial for future studies.