Rethinking the Role of LLM s for Document-level Relation Extraction: a Refiner with Task Distribution and Probability Fusion (2025)

Abstract: Document-level relation extraction (DocRE) provides a broad context for extracting one or more relations for each entity pair. Large language models (LLMs) have made great progress in relation extraction tasks. However, one of the main challenges we face is that LLMs have difficulty in multi-label relation prediction tasks. Additionally, another noteworthy challenge and discovery we reveal: the small language models (SLMs) for DocRE tend to classify existing relations as ”no relation” (NA), while LLMs tend to predict existing relations for all entity pairs. To address these challenges, we propose a novel method that utilizes LLMs as a refiner, employing task distribution and probability fusion. The task distribution we carefully designed aims to distinguish hard and easy tasks, and feed hard tasks to our LLMs-based framework to reevaluate and refine. Further, in order to effectively solve the multi-label relation prediction problem in the refinement process, we propose a probability fusion method, ensuring and enhancing fusion predictions by maintaining a balance between SLMs and LLMs. Extensive experiments on widely-used datasets demonstrate that our method outperforms existing LLMbased methods without fine-tuning by an average of 25.2% F1. Refining SLMs using our method consistently boosts the performance of the SLMs, achieving new state-of-the-art results compared to existing SLMs and LLMs. Our code: https://github.com/Drasick/Drell.

## introduction relation extraction (re) is the task of extracting semantic relations among entities within a given text, which has abundant applications such as knowledge graph construction, question answering, and text analysis @xcite @xcite . prior studies mostly focus 1 our code: https://github.com/drasick/drell . recent advancements in docre focus on the use of neural models for sequence-based, graph-based, and transformer-based approaches @xcite . meanwhile, large language models (llms) have achieved significant success in a wide range of natural language processing tasks, leveraging emergent capabilities of reasoning and in-context learning across diverse domains such as commonsense reasoning and open-domain question answering @xcite . recent studies have utilized llms for relation extraction tasks, including few-shot re @xcite @xcite and sentence-level re @xcite . methods that involve designing prompts and fine-tuning llms for specific tasks have been shown to outperform fine-tuned small language models (slms) in several relation extraction domains @xcite . however, there are few studies on applying llms to docre, including promptre @xcite and docgnre @xcite without fine-tuning, and autore @xcite with fine-tuning. building on our in-depth research, we identify several key focal points that warrant attention and resolution: (i) llms struggle to handle datasets with a large number of negative samples effectively. our research reveals that the limited predictive performance of slms owing to its tendency to classify existing relations as "no relation" (na), while llms tend to predict existing relations for all entity pairs. (ii) llms, which are essentially generative models, struggle with multi-label relation prediction tasks. when multiple relations need to be predicted for an entity pair, the answer generated by llms may not exactly match the relation labels. even when llms are employed to choose from multiple labels, they prefer to choose a single label. to address these points, aimed at exploratory of the ability differed between slms and llms for docre, we propose a method that utilizes llms as a refiner, employing task distribution and probability fusion to complete the docre task. the task distribution initially uses slms to address the issue of a large number of negative samples and subsequently uses llms as a refiner to resolve tasks that are difficult for slms. the probability fusion provides a stable solution to the multi-label classification problem, ensuring that the predictions are not overly dependent on either the slms or the llms. our contributions are as follows: ## methodology our refining method consists of task distribution and self-supervised probability fusion as shown in figure 4 . ## conclusion in this paper, we present a noteworthy finding that llms tend to predict the existence of relations for all entity pairs, while slms often classify existing relations as na. we propose a novel method utilizing llms as refiners, employing task distribution and self-supervised probability fusion. our refiner demonstrates significant improvement over slms and llms methods and achieves state-of-the-art performance. our methods do not require retraining of llms and can be flexibly integrated with various slm approaches on docre, incurring an acceptable cost, while significantly enhancing the performance of all slms' original capabilities.