Shortcut Learning of Large Language Models in Natural Language Understanding
Proposes methods to address shortcut learning in LLMs, significantly improving OOD generalization and adversarial robustness.
Key Findings
Methodology
The paper reviews shortcut learning detection and mitigation methods for LLMs, including feature attribution analysis and invariant risk minimization. Focus is on medium-sized LLMs using pre-training and fine-tuning paradigms.
Key Results
- BERT accuracy drops over 20% on OOD test sets, revealing reliance on training data biases.
- RoBERTa outperforms BERT by ~20% on HANS, showing the impact of different pre-training objectives.
- Data reweighting methods significantly improve performance on hard samples, enhancing generalization.
Significance
This study highlights the reliance of LLMs on non-robust features in NLU tasks, addressing critical challenges in real-world applications with distribution shifts.
Technical Contribution
Introduced a shortcut learning detection framework combining feature attribution and instance attribution analysis. Evaluated the impact of model size and pre-training objectives on robustness.
Novelty
First systematic analysis of shortcut learning in LLMs and its impact on robustness, proposing innovative mitigation methods based on invariant risk minimization.
Limitations
- Current methods address known biases but may not eliminate unknown biases.
- Data augmentation could introduce new biases, affecting performance.
Future Work
Future research could explore more general bias detection methods and integrate causal inference into training frameworks.
AI Executive Summary
Large language models (LLMs) have achieved remarkable success in natural language understanding (NLU) tasks, but their reliance on dataset biases and shortcuts significantly hampers their robustness and generalization.
This paper systematically reviews shortcut learning phenomena in LLMs, revealing their dependence on non-robust features such as lexical and positional biases. These behaviors lead to poor performance under distribution shifts or adversarial attacks. Methods like feature attribution analysis and invariant risk minimization are used to uncover the root causes.
Proposed mitigation strategies include dataset refinement, sample reweighting, and environment-based training. These approaches significantly improve OOD generalization and robustness, offering valuable directions for future research.
Deep Analysis
Background
Natural language understanding (NLU) tasks like NLI, QA, and reading comprehension are central to AI. Models like BERT and RoBERTa excel in these tasks but rely heavily on training data distribution, limiting their real-world applicability.
Core Problem
LLMs often capture non-robust features such as lexical and positional biases during training, leading to poor performance under distribution shifts or adversarial attacks. Addressing this is critical for broader adoption.
Innovation
The paper introduces a systematic analysis framework combining feature attribution, instance attribution, and invariant risk minimization to tackle shortcut learning. It also evaluates the impact of pre-training objectives and model size on robustness.
Methodology
- �� Feature attribution analysis to detect reliance on non-robust features.
- �� Invariant risk minimization by partitioning training data into environments to encourage robust feature learning.
- �� Dataset refinement and sample reweighting to reduce biases in training data.
Experiments
Experiments use datasets like MNLI and HANS to evaluate IID and OOD performance. Comparisons include BERT, RoBERTa, and ELECTRA, with additional tests on data augmentation and reweighting.
Results
Results show RoBERTa outperforms BERT by ~20% on HANS. Data reweighting methods improve performance on hard samples, enhancing generalization.
Applications
Findings improve robustness in applications like intelligent assistants and machine translation, especially under distribution shifts.
Limitations & Outlook
Current methods primarily address known biases and may fail to detect unknown ones. Data augmentation risks introducing new biases.
Plain Language Accessible to non-experts
Imagine LLMs as students who memorize answers instead of understanding concepts. This works well for familiar questions but fails for new ones. The research aims to make these 'students' truly understand the material, improving their adaptability.
ELI14 Explained like you're 14
Think of playing a game where characters follow simple rules, like avoiding red objects. If the rules change, these characters fail! Scientists found LLMs have similar issues—they rely on shortcuts instead of understanding. This research helps make them smarter to handle rule changes.
Glossary
Shortcut Learning
Models rely on non-robust features from data instead of learning true semantic understanding.
Used to analyze LLM performance in NLU tasks.
Invariant Risk Minimization
Encourages models to learn robust features across multiple environments.
Mitigates data bias issues.
Feature Attribution
Analyzes input features' contributions to model predictions.
Detects reliance on non-robust features.
Lexical Bias
Models depend on statistical correlations between certain words and labels.
Prominent in NLI tasks.
Data Reweighting
Adjusts training sample weights to reduce bias reliance.
Improves model generalization.
Open Questions Unanswered questions from this research
- 1 How to detect unknown biases? Current methods focus on identified biases.
- 2 How to integrate causal inference to enhance robustness?
Applications
Immediate Applications
Optimizing Intelligent Assistants
Improves performance of Alexa, Siri, etc., under distribution shifts.
Question Answering Systems
Enhances understanding of complex queries, reducing reliance on lexical biases.
Long-term Vision
General Robust Language Models
Develop models adaptable to diverse distribution shifts, transforming AI applications.
Abstract
Large language models (LLMs) have achieved state-of-the-art performance on a series of natural language understanding tasks. However, these LLMs might rely on dataset bias and artifacts as shortcuts for prediction. This has significantly affected their generalizability and adversarial robustness. In this paper, we provide a review of recent developments that address the shortcut learning and robustness challenge of LLMs. We first introduce the concepts of shortcut learning of language models. We then introduce methods to identify shortcut learning behavior in language models, characterize the reasons for shortcut learning, as well as introduce mitigation solutions. Finally, we discuss key research challenges and potential research directions in order to advance the field of LLMs.