Constrained Generation based Data Augmentation for Low-Resource Natural Language Processing

Core Concepts

CoDa, a controllable and effective data augmentation technique for low-resource NLP, generates synthetic training instances by prompting off-the-shelf instruction-following Large Language Models to produce text that satisfies a set of simple constraints extracted from the original low-resource dataset.

Abstract

The paper presents CoDa, a novel data augmentation methodology for low-resource Natural Language Processing (NLP) tasks. CoDa works with any off-the-shelf instruction-tuned Large Language Model (LLM) in a training-free fashion and provides explicit control over the generated augmentations. The key steps are: For each training instance in the low-resource dataset, CoDa extracts a set of simple heuristic-based constraints, including lexical, syntactic, semantic, length, and concept constraints. These constraints are then verbalized into a natural language instruction and used to prompt an LLM to generate augmented training instances. The generated augmentations are then added to the original low-resource dataset to train downstream NLP models. CoDa is shown to outperform various prior data augmentation techniques, including text-editing, learning-based infilling, LLM-based prompting, and rephrasing methods, across 11 datasets spanning 3 tasks (Sequence Classification, Named Entity Recognition, and Question Answering) and 3 low-resource settings (100, 200, and 500 training examples). The improvements range from 0.12% to 7.19% in F1 score. CoDa is the first framework to explore controlled generation for data augmentation, ensuring the synthetic data is closely aligned with the specific needs of the task and characteristics of the target domain. It provides a simpler and more intuitive natural language-based interface for constrained generation compared to complex decoding-time techniques or manual attribute extraction.

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Key Insights Distilled From

CoDa

by Chandra Kira... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00415.pdf

Deeper Inquiries

How can CoDa's constraint extraction and verbalization process be further automated and generalized to handle more complex constraints?

CoDa's constraint extraction and verbalization process can be further automated and generalized by incorporating advanced natural language processing techniques. One approach could involve leveraging pre-trained models like BERT or GPT-3 to automatically identify and extract complex constraints from the low-resource dataset. These models can be fine-tuned on a diverse set of constraints to improve accuracy in constraint extraction. Additionally, the verbalization process can be enhanced by developing a more sophisticated template system that can dynamically generate natural language instructions based on the extracted constraints. This could involve using neural text generation models to create coherent and contextually relevant instructions for the large language models to follow. Furthermore, the automation process can benefit from incorporating reinforcement learning techniques to iteratively improve the constraint extraction and verbalization mechanisms. By providing feedback on the quality of generated augmentations, the system can learn to adapt and refine its processes over time.

How can CoDa's performance be improved on tasks that require more sophisticated reasoning and generation capabilities beyond simple constraint satisfaction?

To enhance CoDa's performance on tasks requiring more sophisticated reasoning and generation capabilities, several strategies can be implemented: Fine-tuning on domain-specific data: Training the large language models on domain-specific data related to the task can improve their understanding and generation capabilities in that particular domain. Incorporating external knowledge sources: Integrating external knowledge bases or ontologies can provide additional context and information for the models to generate more accurate and contextually relevant augmentations. Enabling multi-step reasoning: Implementing a multi-step reasoning mechanism where the model can iteratively process and generate text based on intermediate results can improve the overall coherence and quality of the augmentations. Utilizing structured data inputs: For tasks with structured data inputs, converting the data into a format that the models can easily process and reason over can enhance their performance in generating relevant and accurate augmentations. Enabling conditional generation: Implementing conditional generation mechanisms where the model can generate text based on specific conditions or contexts can improve the relevance and accuracy of the generated augmentations.

What are the potential ethical and fairness implications of using CoDa for data augmentation, especially in sensitive domains like healthcare or finance?

Using CoDa for data augmentation in sensitive domains like healthcare or finance raises several ethical and fairness considerations: Bias amplification: If the constraints used for augmentation are biased or reflect existing biases in the dataset, CoDa may inadvertently amplify these biases in the generated data, leading to unfair outcomes. Privacy concerns: Generating synthetic data that closely resembles real data may raise privacy concerns, especially in healthcare where patient data confidentiality is crucial. Care must be taken to ensure that sensitive information is not leaked through the generated augmentations. Regulatory compliance: In domains like finance, where strict regulations govern data usage and processing, using augmented data generated by CoDa may raise compliance issues if the generated data does not adhere to regulatory standards. Transparency and accountability: The use of augmented data in decision-making processes must be transparent, and there should be mechanisms in place to ensure accountability for any decisions made based on the augmented data. Algorithmic fairness: Ensuring that the generated augmentations do not discriminate against certain groups or individuals is essential for maintaining algorithmic fairness. CoDa should be designed and evaluated to mitigate any potential biases in the generated data. Addressing these ethical and fairness implications requires a comprehensive approach that involves careful design, monitoring, and evaluation of the data augmentation process in sensitive domains. Collaboration with domain experts and stakeholders is crucial to ensure that the use of CoDa aligns with ethical standards and regulatory requirements.