D oc IE @ XLLM 25: UIEP rompter: A Unified Training-Free Framework for universal document-level information extraction via Structured Prompt (2025)

Abstract: We introduce UIEPrompter, a unified, training-free framework that secures 1st place in the ACL 2025 shared competition on universal document-level information extraction. UIEPrompter effectively addresses both named entity recognition and relation extraction without the need for annotated data.Leveraging large language models, UIEPrompter establishes a zero-shot baseline through role-specific prompts, which are then refined via few-shot guidance and constrained output generation prompt to align with competition schemas. Additionally, by integrating outputs from several large language models, we reduce individual model biases, thereby improving overall performance. Evaluated on the competition evaluation dataset, UIEPrompter showcases outstanding performance in document-level information extraction, ultimately securing first place. The implementation code is available on GitHub.

## introduction information extraction (ie) @xcite @xcite serves as a critical bridge between unstructured text and structured knowledge representation, enabling the identification of entities, their semantic types, and inter-entity relationships in texts. in recent years, ie has garnered significant attention from both academia and industry. the latest acl 2025 shared task on universal document-level information extraction (docie) presents a novel challenge that requires the extraction of named entities @xcite @xcite and their relations @xcite @xcite @xcite from documents. 1 † equal contribution. 2 * corresponding author. conventional approaches adopt a fragmented pipeline, deploying separate models for named entity recognition (ner) and relation extraction @xcite . while effective in narrow contexts, this paradigm faces two fundamental limitations: (1) a heavy reliance on annotated training data for optimizing both models, and (2) the risk of cascading error propagation between the ner and re stages, where misidentified entities can lead to incorrect relation predictions downstream. to overcome these constraints, we propose uieprompter as shown in figure 1 , a trainingfree framework that unifies ner and re through meticulous prompt engineering and the generative prowess of large language models (llms) @xcite @xcite . the key advantages of our framework are: ## conclusion in this work, we present uieprompter, a unified and training-free framework that secured first place in the acl 2025 shared competition on universal document-level information extraction. our framework adeptly addresses the challenges of named entity recognition and relation extraction in document-level contexts without relying on annotated training data. by leveraging role-specific prompts for zero-shot initialization and adapting to competition schemas through few-shot guidance and constrained output generation prompt, uieprompter demonstrates remarkable information extraction performance and generalization capabilities.