将预定义模板当作可参数化的工具，让大模型自动选型填空，统一处理各类信息抽取任务。
Schema as Parameterized Tools for Universal Information Extraction
- 把信息抽取模板视为可调用的参数化工具，实现动态选择与填充
- 在四种不同抽取任务上表现稳定，性能接近顶尖系统但参数量更少
- 适合需要灵活应对多场景、少训练成本的信息抽取应用
通用信息抽取（UIE）通常使用大语言模型以抽取式生成方式输出结构化信息，依赖预设的如JSON或表格格式的模板。然而，在上下文学习范式下，面对大量可选模板时，现有方法缺乏自适应能力，难以实现即时模板生成。本文提出一种统一的自适应文本到结构生成框架——模式即参数化工具（SPT），重新构想大模型的工具调用能力，将预设模板视为可参数化的工具，用于模板检索、参数填充或新模板生成。SPT可统一处理封闭式、开放式及按需生成的抽取任务：通过从预设池中检索相关模板进行模式检索，提取信息并填入槽位作为参数填充，或对未覆盖情况合成新模板。实验表明，SPT能自适应处理四种不同类型的抽取任务，展现出稳健的模板检索与选择能力；其抽取性能与LoRA基线及当前领先UIE系统相当，但可训练参数显著减少。
原文摘要 · Abstract (English)
Universal information extraction (UIE) primarily employs an extractive generation approach with large language models (LLMs), typically outputting structured information based on predefined schemas such as JSON or tables. UIE suffers from a lack of adaptability when selecting between predefined schemas and on-the-fly schema generation within the in-context learning paradigm, especially when there are numerous schemas to choose from. In this paper, we propose a unified adaptive text-to-structure generation framework, called Schema as Parameterized Tools (SPT), which reimagines the tool-calling capability of LLMs by treating predefined schemas as parameterized tools for tool selection and parameter filling. Specifically, our SPT method can be applied to unify closed, open, and on-demand IE tasks by adopting Schema Retrieval by fetching the relevant schemas from a predefined pool, Schema Filling by extracting information and filling slots as with tool parameters, or Schema Generation by synthesizing new schemas with uncovered cases. Experiments show that the SPT method can handle four distinct IE tasks adaptively, delivering robust schema retrieval and selection performance. SPT also achieves comparable extraction performance to LoRA baselines and current leading UIE systems with significantly fewer trainable parameters.
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