Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless Language Models (2311.11202v2)

Abstract: LLMs have shown promise in various tasks but can be affected by undesired data during training, fine-tuning, or alignment. For example, if some unsafe conversations are wrongly annotated as safe ones, the model fine-tuned on these samples may be harmful. Therefore, the correctness of annotations, i.e., the credibility of the dataset, is important. This study focuses on the credibility of real-world datasets, including the popular benchmarks Jigsaw Civil Comments, Anthropic Harmless & Red Team, PKU BeaverTails & SafeRLHF, that can be used for training a harmless LLM. Given the cost and difficulty of cleaning these datasets by humans, we introduce a systematic framework for evaluating the credibility of datasets, identifying label errors, and evaluating the influence of noisy labels in the curated language data, specifically focusing on unsafe comments and conversation classification. With the framework, we find and fix an average of 6.16% label errors in 11 datasets constructed from the above benchmarks. The data credibility and downstream learning performance can be remarkably improved by directly fixing label errors, indicating the significance of cleaning existing real-world datasets. We provide an open-source tool, Docta, for data cleaning at https://github.com/Docta-ai/docta.

• The paper introduces a novel framework to identify and correct label errors using noise transition matrices and k-NN label clusterability.
• It demonstrates that cleansing errors, with an average noise rate of 6.16% across datasets, significantly boosts performance in BERT and GPT-2 models.
• It shows practical cost benefits by reducing human annotation efforts by up to 90%, ensuring safer and more reliable language models.

Essay on "Unmasking and Improving Data Credibility"

The paper "Unmasking and Improving Data Credibility: A Study with Datasets for Training Harmless LLMs" presents a rigorous methodological framework designed to evaluate and enhance the credibility of LLM training datasets by identifying and correcting label errors. This research addresses a crucial issue in the development of AI systems—ensuring that LLMs are trained on accurately annotated data to prevent harmful outputs.

Overview

The paper focuses on datasets commonly used for training LLMs to ensure they produce non-harmful content. It highlights the significant impact of erroneous or biased annotations on model performance. The researchers propose a systematic approach to evaluate data credibility, scrutinizing popular benchmarks such as Jigsaw Civil Comments, Anthropic Harmless, and others. By tackling errors in annotations, the paper aims to improve the downstream learning performance of LLMs.

Key Contributions

1. Data Credibility Framework: The authors introduce a framework for assessing dataset credibility without requiring true labels. This is achieved through estimating noise transition matrices and leveraging $k$-NN label clusterability. Such a framework can quantitatively measure and potentially rectify label inaccuracies, crucial for ensuring data integrity.
2. Detection and Correction of Label Errors: The research identifies an average of 6.16% label errors across 11 analyzed datasets, with individual datasets showing noise rates from 2% to over 15%. By addressing these errors, significant improvements in model performance were observed on both BERT and GPT-2 models, with the performance metrics such as F1-score showing notable enhancements.
3. Practical Implications: The paper underscores the practicality of automated data cleaning, highlighting potential cost reductions in human annotation efforts. For instance, in the Civil Comments dataset, human effort could be reduced by approximately 90% when using the proposed cleaning pipeline.
4. Algorithmic Implementation: The proposed methods are available openly, providing researchers and practitioners with tools to enhance data reliability in their models. This encourages broader adoption and further research in improving dataset credibility for AI safety alignment.

Results and Implications

Quantitative evaluation of the framework showed that correcting label errors led to substantial improvements in model accuracy and reliability. This suggests that preemptive data cleaning should be considered as a standard practice in model training pipelines. The correction of label errors, verified with human re-annotation and cross-validation with other models like ChatGPT, supports the framework's reliability.

The improved data credibility contributes to the safer deployment of LLMs, aligning them closer to human ethical standards and reducing the risks associated with biased or harmful outputs. Moreover, the approach complements reinforcement learning methods for human feedback, potentially reducing biases introduced during dataset curation.

Future Directions

Moving forward, the implications of this research suggest several interesting avenues. Further exploration of adaptive methods for real-time data credibility assessment during model training could enhance ongoing learning systems. Additionally, investigating the role of different model architectures in handling corrected datasets could provide deeper insights into model-specific sensitivity to label noise.

The paper opens pathways for integrating data credibility evaluation in larger frameworks for AI safety and ethical compliance, underscoring the need for robust, transparent, and verifiable methods in AI development. By addressing data integrity at the source, researchers can significantly impact the safety and trustworthiness of AI systems across various applications.