Teacher-Student Training for Debiasing: General Permutation Debiasing for Large Language Models

المفاهيم الأساسية

Efficient teacher-student training can improve performance and reduce bias in large language models.

الملخص

This article explores the sensitivity of Large Language Models (LLMs) to input order and biases, proposing debiasing techniques through teacher-student training. It addresses the computational inefficiencies of permutation debiasing and introduces distillation and error-correction approaches for student models. The study demonstrates improved performance with fewer parameters using these methods on various tasks like multiple-choice question answering and comparative assessment.

1. Introduction

   • LLMs excel at unseen tasks but exhibit biases.
   • Permutation sensitivity affects task performance.

2. Multiple Choice Prompting

   • Prompting enhances zero-shot abilities of LLMs.
   • Multiple choice classification benefits from ordered realizations of answers.

3. Inherent Biases in LLMs

   • Sensitivity to permutations impacts decision-making.
   • Quantifying bias helps measure model performance.

4. Debiasing Approaches

   • Permutation debiasing ensembles all permutations.
   • Prior-matching minimizes positional bias.

5. Teacher-Student Training for Debiasing

   • Distillation trains students to emulate debiased teachers.
   • Error correction corrects biased decisions for complex tasks.

6. Experimental Set Up

   • Experiments conducted on MCQA and SummEval datasets.
   • Base language models include FlanT5 and Llama2-chat.

7. Results

   • Debiased student models outperform biased teachers.
   • Error correction students effectively leverage biased samples.

8. Limitations

   • Framework demonstrated effective on specific tasks only.
   • Requires access to unlabelled examples during training.

9. Acknowledgements
   Research supported by Cambridge University Press & Assessment and Gates Cambridge Trust.

الإحصائيات

Large Language Models (LLMs) have impressive zero-shot capabilities.
Teacher-student training improves performance with fewer parameters.

اقتباسات

"Large Language Models (LLMs) suffer from particular limitations."
"Debiasing approaches can mitigate biases in LLMs."

الرؤى الأساسية المستخلصة من

Teacher-Student Training for Debiasing

by Adian Liusie... في arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13590.pdf

استفسارات أعمق

How can the teacher-student framework be applied to other types of biases in language models?

The teacher-student framework can be adapted to address various biases present in language models beyond just permutation sensitivity. For instance, if a model exhibits gender bias or racial bias in its outputs, the same framework can be utilized by training a student model to emulate a debiased version of the teacher's predictions. By distilling the knowledge from a debiased teacher model into a more compact student model, it is possible to mitigate these biases and improve overall performance and reliability. The key lies in identifying the specific bias that needs correction and designing appropriate training strategies for the student model.

What are the implications of reducing computational costs in debiasing techniques?

Reducing computational costs in debiasing techniques has several significant implications. Firstly, it makes these techniques more accessible and practical for real-world applications where efficiency is crucial. By streamlining the process and making it less computationally intensive, researchers and developers can implement debiasing methods more easily across different platforms and scenarios. This increased accessibility could lead to wider adoption of debiasing techniques, ultimately improving fairness, accuracy, and trustworthiness in AI systems.
Additionally, lowering computational costs opens up opportunities for faster experimentation and iteration on different debiasing strategies. Researchers can explore a broader range of approaches without being hindered by high computational demands, potentially leading to more innovative solutions for addressing biases in language models.
Overall, reducing computational costs enhances the scalability and usability of debiasing techniques within AI systems, paving the way for more ethical and inclusive applications of machine learning technologies.

How might the findings of this study impact the development of future language models?

The findings of this study carry several implications for future developments in language modeling:

Improved Model Robustness: By highlighting issues such as permutation sensitivity and providing effective solutions through teacher-student frameworks, future language models can be designed with enhanced robustness against biases related to input variations.

Efficient Training Strategies: The use of distillation or error-correction approaches demonstrated in this study offers insights into efficient training strategies that balance performance with reduced computational overheads. Future language models could benefit from similar methodologies to achieve better results with fewer resources.

Generalization Across Tasks: Understanding how different tasks affect bias sensitivity allows for task-agnostic approaches towards mitigating biases effectively across various applications involving natural language processing tasks.

Ethical Considerations: As AI ethics become increasingly important, incorporating mechanisms like those proposed here will ensure that future language models prioritize fairness while maintaining high performance standards.

In conclusion,
the study's findings provide valuable guidance on enhancing future language models' capabilities while promoting fairness
and efficiency within AI systems.