Addressing Definition Bias in Information Extraction: Probing Experiments and Mitigation Framework

核心概念

Mitigating definition bias in information extraction is crucial for improving model performance and alignment with annotations.

要約

The content discusses the issue of definition bias in information extraction, highlighting biases among datasets and instruction tuning. It proposes a multi-stage framework to address this bias, including measurement, bias-aware fine-tuning, and task-specific mitigation. Probing experiments reveal challenges and limitations in current approaches.
Directory:

Introduction

Definition of Bias in Machine Learning
Challenges in Large Language Models (LLMs) for Information Extraction

Types of Definition Bias in Information Extraction

Bias Among Datasets
Bias Between Instruction Tuning Datasets

Investigating Definition Bias through Probing Experiments

Cross-Validation Experiment Results
Source Prompt Tuning Process
Source Prompt Inference Results

Addressing Definition Bias with a Multi-Stage Framework

Definition Bias Measurement using Fleiss' Kappa
Bias-Aware Fine-Tuning Approach
Task-Specific Bias Mitigation with Low-Rank Adaptation (LoRA)

Experimental Validation of the Two-Stage Framework

Comparative Results with Baseline Models
Ablation Study on Two-Stage Fine-Tuning Effectiveness

Related Work on LLMs for Information Extraction and Universal Information Extraction

Conclusion and Limitations

統計

"Experimental results demonstrate the effectiveness of our framework in addressing definition bias."
"Table 1: Definition bias among different NER tasks."
"Table 2: Definition bias among different RE tasks."
"Table 3: Different extraction results obtained by prompting the source prompt tuning UIE with true, nickname, and fake source name."
"Table 4: Performance of Open-source LLM and close-source LLM on various information extraction tasks in (zero-shot | few-shot) settings."
"Table 5: Main result for comparing with other models on NER and RE tasks."

引用

"Definition bias negatively impacts the transferability of a fully-supervised model."
"Our probing experiments reveal challenges in addressing definition bias effectively."
"The two-stage fine-tuning framework consistently improves performance across specific datasets."

抽出されたキーインサイト

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

by Wenhao Huang... 場所 arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16396.pdf

深掘り質問

How can we ensure that the proposed framework remains effective across diverse datasets?

To ensure that the proposed framework remains effective across diverse datasets, several strategies can be implemented:

Dataset Diversity: Incorporate a wide range of datasets from different domains and with varying annotation schemas to train the model. This will help in capturing a broader spectrum of definitions and reducing bias.
Regular Evaluation: Continuously evaluate the performance of the framework on new datasets to identify any emerging biases or limitations. Regular updates and adaptations based on these evaluations will help maintain effectiveness.
Adaptive Fine-Tuning: Implement adaptive fine-tuning techniques that can adjust model parameters based on dataset characteristics, ensuring flexibility and adaptability across diverse data sources.
Robust Evaluation Metrics: Utilize robust evaluation metrics that account for variations in dataset characteristics, ensuring fair assessment of model performance across different datasets.

What are the potential implications of not addressing definition bias in information extraction models?

The implications of not addressing definition bias in information extraction models include:

Inaccurate Results: Definition bias can lead to inaccurate extraction results as models may favor certain interpretations over others, impacting the reliability and quality of extracted information.
Reduced Generalizability: Models affected by definition bias may struggle to generalize well beyond their training data, limiting their applicability to real-world scenarios outside specific contexts.
Misguided Decision-Making: Biased extraction results could potentially misguide decision-making processes based on extracted information, leading to errors or incorrect conclusions.
Ethical Concerns: Unaddressed biases in information extraction models could perpetuate existing societal biases or stereotypes present in the training data, raising ethical concerns about fairness and equity.

How might biases present within instruction tuning datasets impact real-world applications beyond information extraction?

Biases within instruction tuning datasets can have far-reaching impacts beyond information extraction tasks:

Automated Decision Systems: Biases in instruction tuning datasets could propagate into automated decision-making systems trained using these biased instructions, leading to discriminatory outcomes in areas like hiring processes or loan approvals.
Natural Language Understanding Applications: Biases present during instruction tuning may affect natural language understanding applications such as chatbots or virtual assistants, influencing how they interpret user queries and provide responses.
Personalization Algorithms: In recommendation systems or personalized content delivery platforms relying on instruction tuning data, biases could result in skewed recommendations tailored based on biased instructions rather than genuine user preferences.
4Legal Implications: If biased instructions lead to incorrect legal interpretations by AI-powered legal research tools or contract analysis systems used by law firms, it could have serious legal consequences affecting court cases or business contracts.