XNLP: An Interactive Demonstration System for Universal Structured NLP
This addresses the problem for NLP researchers by offering a more universal and interactive demonstration platform, though it appears incremental as it builds on existing LLM capabilities.
The authors tackled the lack of a comprehensive and interactive demonstration system for universal structured NLP tasks by proposing XNLP, a platform that leverages LLMs to achieve high generalizability across all XNLP tasks, providing a unified online tool for the community.
Structured Natural Language Processing (XNLP) is an important subset of NLP that entails understanding the underlying semantic or syntactic structure of texts, which serves as a foundational component for many downstream applications. Despite certain recent efforts to explore universal solutions for specific categories of XNLP tasks, a comprehensive and effective approach for unifying all XNLP tasks long remains underdeveloped. In the meanwhile, while XNLP demonstration systems are vital for researchers exploring various XNLP tasks, existing platforms can be limited to, e.g., supporting few XNLP tasks, lacking interactivity and universalness. To this end, we propose an advanced XNLP demonstration platform, where we propose leveraging LLM to achieve universal XNLP, with one model for all with high generalizability. Overall, our system advances in multiple aspects, including universal XNLP modeling, high performance, interpretability, scalability, and interactivity, providing a unified platform for exploring diverse XNLP tasks in the community. XNLP is online: https://xnlp.haofei.vip