Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases (2024)

Abstract: Definition bias is a negative phenomenon that can mislead models. However, definition bias in information extraction appears not only across datasets from different domains but also within datasets sharing the same domain. We identify two types of definition bias in IE: bias among information extraction datasets and bias between information extraction datasets and instruction tuning datasets. To systematically investigate definition bias, we conduct three probing experiments to quantitatively analyze it and discover the limitations of unified information extraction and large language models in solving definition bias. To mitigate definition bias in information extraction, we propose a multi-stage framework consisting of definition bias measurement, bias-aware fine-tuning, and task-specific bias mitigation. Experimental results demonstrate the effectiveness of our framework in addressing definition bias.

## introduction bias in machine learning refers to systematic errors in predictions in the machine learning process, such as annotator bias, measurement bias, etc @xcite . in the era of large language models (llms), these issues are addressed by filtering low-quality corpora @xcite and training with human preferences @xcite . however, performance remains subpar in handling information extraction (ie) tasks @xcite , which we believe is due to definition bias. definition bias in ie refers to the tendency of an information extraction system to favour certain interpretations of data over others, often due to the way concepts, entities, or relationships are defined within the system. as the fast development of unified information extraction (uie) @xcite 2022) and large language models (llms) @xcite @xcite in recent years, two novel definition bias emerge, which are: bias among ie datasets and bias between ie and instruction tuning (ift) datasets. regarding bias among ie datasets, it refers to the definition differences between different data sets under the same annotation schema. as illustrated in figure 1 , different datasets have different annotations to the same input for both named entity recognition (ner) and relation extraction (re) tasks. regarding bias between ie and instruction tuning datasets, it highlights the mismatch between the information extraction task and the general task. as depicted in figure 1 , although @xcite is capable of extracting entities or relational triples in accordance with the specified task description without providing extra examples, its prediction differs from those in the existing datasets. to systematically investigate definition bias in ie, we devise a series of probing experiments. first, we analyze whether definition bias exists and how it varies among datasets sharing the same tasks. by conducting cross-validation experiments among various datasets in the ner and re, we observe a significant decrease in performance, indicating that definition bias negatively impacts the transferability of a fully-supervised model. an intuitive way to alleviate definition bias is unified information extraction, which is trained across multiple ie datasets. therefore, we analyze in the unified information extraction setting, does definition bias still exist? by introducing source prompt @xcite that applies true or fake source names for the uie models, we discover the inconsistency of the uie for extraction, which indicates that uie suffers from definition bias among ie datasets. the other way to mitigate definition bias is llms, which can understand a wide range of human instructions. thereupon, we analyze can llms address the challenge of definition bias? by conducting experiments on few-shot settings on ner and re tasks with in-context learning, we find that it's difficult for llms without parameter updates to attain satisfactory performance, which indicates that llms still suffer from definition bias between ie and instruction fine-tuning datasets. according to our probing experiments, it is imperative to address definition bias by proposing a universal solution for ie tasks. however, mitigating definition bias is non-trivial, primarily owing to the following three challenges. (1) enhancing the capacity of llms in general information extraction tasks is vital to reduce the definition bias between information extraction datasets and instruction tuning datasets; (2) mitigating the definition bias during the tuning of llms with different ie datasets; to address these challenges, we propose a framework to alleviate definition bias, which consists of definition bias measurement, bias-aware finetuning and task-specific bias mitigation. using fleiss's kappa @xcite , we measure the two types of definition bias above. then we conduct bias-aware fine-tuning with multiple information extraction instructions to enhance the extraction capabilities with less definition bias. ultimately, we conduct the task-specific bias mitigation, with low-rank adaptation technique (lora) @xcite for specific information extraction tasks to further align the llms with annotations. our paper is organized as follows: in section 3, we present three probing experiments designed to explore the presence of definition bias and assess the ability of existing frameworks to address this issue in ie. section 4 details the results and analysis of these experiments, concluding that frameworks based on either one-stage processing or parameterfree updates are insufficient to tackle definition bias in ie. consequently, we propose a novel framework featuring two-stage fine-tuning, specifically developed to mitigate the identified definition bias, as introduced in section 5. ultimately, in section 6, we compare the performance of our framework with state-of-the-art methods in universal information extraction, demonstrating its effectiveness in reducing definition bias. ## definition bias probing experiment we initially propose an experiment employing cross-validation to investigate the presence of definition bias in the ie tasks. subsequently, we design two specific detection tasks: source prompt detection and few-shot prompting in llms, to examine two categories of definition bias: bias within ie datasets and bias between ie and instruction finetuning datasets. these experiments aim to explore the effectiveness of the uie and llm frameworks in addressing the definition bias issue. ## alleviate definition bias in this section, we explore methods to enhance the information extraction capabilities of llms. based on the probing experiments and conclusions outlined in sections 3 and 4, we find that definition bias across different datasets significantly impacts the performance of uie and llms. this indicates that a framework relying solely on a onestage, parameter-free update is inadequate for addressing definition bias. to tackle this challenge, we introduce a two-stage fine-tuning framework. moreover, by identifying and explicitly quantifying the two types of definition biases we have discovered, we can integrate these measurements into our fine-tuning framework, effectively reducing the influence of definition bias. ## experiments of two-stage framework this section conducts experiments to validate the effectiveness of our two-stage fine-tuning framework. we select 11b flan-t5 @xcite as our backbone model. the details of the experimental setup and comparison methods are described in the following parts. ## conclusion in the paper, we propose the definition bias problem in information extraction tasks. we conduct several probing experiments to comprehensively demonstrate that existing methods cannot address definition bias. we then propose a multi-stage tuning framework, which consists of bias-aware finetuning and task-specific bias mitigation, to alleviate the definition bias in a specific dataset. experimental results show that our framework is efficient in mitigating definition bias.