Developing a general-purpose system that can extract events with massive types is a long-standing target in Event Extraction (EE). In doing so, the basic challenge comes from the absence of an efficient and effective annotation framework to construct the corresponding datasets. In this paper, we propose an LLM-based collaborative annotation framework. Through collaboration among multiple LLMs and a subsequent voting process, it refines annotations of triggers from distant supervision and then carries out argument annotation. Finally, we create EEMT, the largest EE dataset to date, featuring over **200,000** samples, **3,465** event types, and **6,297** role types. Evaluation on human-annotated test set demonstrates that the proposed framework achieves the F1 scores of **90.1%** and **85.3%** for event detection and argument extraction, strongly validating its effectiveness. Besides, to alleviate the excessively long prompts caused by massive types, we propose an LLM-based Partitioning method for EE called LLM-PEE. It first recalls candidate event types and then splits them into multiple partitions for LLMs to extract. After fine-tuning on the EEMT training set, the distilled LLM-PEE with 7B parameters outperforms state-of-the-art methods by **5.4%** and **6.1%** in event detection and argument extraction. Besides, it also surpasses mainstream LLMs by **12.9%** on the unseen datasets, which strongly demonstrates the event diversity of the EEMT dataset and the generalization capabilities of the LLM-PEE method.

Empirical evidence indicates that LLMs exhibit spontaneous cross-lingual alignment. However, although LLMs show promising cross-lingual alignment in Information Extraction (IE), a significant imbalance across languages persists, highlighting an underlying deficiency. To address this, we propose KnowCoder-X, a powerful code LLM with advanced cross-lingual and multilingual capabilities for universal IE. Firstly, it standardizes the representation of multilingual schemas using Python classes, ensuring a consistent ontology across different languages. Then, IE across languages is formulated as a unified code generation task. Secondly, we conduct IE cross-lingual alignment instruction tuning on the translated instance prediction task to enhance the model’s cross-lingual transferability. During this phase, we also construct a high-quality and diverse bilingual IE parallel dataset with 257k samples, called ParallelNER, synthesized by our proposed robust three-stage pipeline, with manual annotation to ensure quality. Although without training in 29 unseen languages, KnowCoder-X surpasses ChatGPT by 30.17% and SoTA by 20.03%, thereby demonstrating superior cross-lingual IE capabilities. Comprehensive evaluations on 64 IE benchmarks in Chinese and English under various settings demonstrate that KnowCoder-X significantly enhances cross-lingual IE transfer through boosting the IE alignment. Our code and dataset are available at: https://github.com/ICT-GoKnow/KnowCoder.

This paper presents an in-depth study of multimodal machine translation (MMT), examining the prevailing understanding that MMT systems exhibit decreased sensitivity to visual information when text inputs are complete. Instead, we attribute this phenomenon to insufficient cross-modal interaction, rather than image information redundancy. A novel approach is proposed to generate parallel Visual Question-Answering (VQA) style pairs from the source text, fostering more robust cross-modal interaction. Using Large Language Models (LLMs), we explicitly model the probing signal in MMT to convert it into VQA-style data to create the Multi30K-VQA dataset. An MMT-VQA multitask learning framework is introduced to incorporate explicit probing signals from the dataset into the MMT training process. Experimental results on two widely-used benchmarks demonstrate the effectiveness of this novel approach. Our code and data would be available at: https://github.com/libeineu/MMT-VQA.