Evaluating and Mitigating Linguistic Bias in Large Language Models

Core Concepts

Large language models exhibit linguistic discrimination, providing inconsistent responses and safety levels across different languages due to uneven training data distribution.

Abstract

The study explores linguistic discrimination in large language models (LLMs) from two perspectives: safety and quality. Safety Assessment: Evaluates the consistency of LLMs' responses to harmful queries across 74 languages. Four LLMs (Llama2-13b, Gemma-7b, GPT-3.5-turbo, Gemini-pro) are tested using the AdvBench dataset. Results show significant variations in the jailbreak success rate across languages, with high-resource languages like English, French, Russian, and Spanish performing much better than low-resource languages like Bengali, Georgian, Nepali, and Maithili. Quality Assessment: Examines the quality of LLMs' responses to benign queries across 74 languages using the NQ dataset. Measures response quality using F1-score. Finds that LLMs generally provide higher-quality responses in high-resource languages like English, Danish, Czech, and Slovenian compared to low-resource languages like Kannada, Southern Pashto, Tajik, and Telugu. Overall, the study demonstrates that both open-source and commercial state-of-the-art LLMs exhibit significant linguistic discrimination, posing risks to marginalized language communities.

Stats

The average jailbreak rate for harmful queries is 1.04% in high-resource languages, but 27.7% in low-resource languages. The average F1-score for benign queries is 0.1494 in high-resource languages, but only 0.0341 in low-resource languages.

Quotes

"LLMs acquire their multilingual capabilities through training on diverse datasets encompassing multiple languages. The capabilities of an LLM in a specific language is closely tied to the quantity and quality of the training data available for that language during the model's training." "Our results indicate that the four LLMs exhibit the strongest human alignment capabilities when processing queries in English, French, Russian, and Spanish, with an average jailbreak rate of only 1.04% for harmful queries, but show the weakest performance when handling queries in Bengali, Georgian, Nepali, and Maithili, with an average jailbreak rate of 27.7% for harmful queries." "For queries in English, Danish, Czech and Slovenian, LLMs tend to generate higher-quality responses, with an average F1-score of 0.1494. By contrast, the average F1-score in Kannada, Southern Pashto, Tajik and Telugu is only 0.0341 on average."

Key Insights Distilled From

Evaluating and Mitigating Linguistic Discrimination in Large Language Models

by Guoliang Don... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18534.pdf

Deeper Inquiries

How can we effectively fine-tune or augment LLMs to improve their performance on low-resource languages without compromising their capabilities on high-resource languages?

To enhance the performance of Large Language Models (LLMs) on low-resource languages without sacrificing their capabilities on high-resource languages, several strategies can be employed: Data Augmentation: One approach is to augment the training data for low-resource languages. This can involve techniques such as back-translation, where sentences in the low-resource language are translated into a high-resource language and then back-translated to the original language. This process helps generate additional training data for the low-resource language. Transfer Learning: Utilizing transfer learning can be beneficial. Pre-train the LLM on a large dataset that includes both high and low-resource languages, and then fine-tune the model on specific tasks or datasets for the low-resource language. This way, the model can leverage the knowledge gained from high-resource languages while adapting to the nuances of the low-resource language. Multilingual Training: Train the LLM on a diverse dataset that includes a balanced representation of high and low-resource languages. By exposing the model to a wide range of languages during training, it can learn to generalize better across languages and perform well on both high and low-resource languages. Language-Specific Fine-Tuning: After pre-training on a multilingual dataset, fine-tune the model on specific tasks or datasets for low-resource languages. This fine-tuning process allows the model to specialize in the nuances of the low-resource language while retaining its overall multilingual capabilities. Collaboration and Resource Sharing: Collaboration between researchers, organizations, and communities speaking low-resource languages can help pool resources, data, and expertise to collectively improve the performance of LLMs on these languages. Sharing resources and knowledge can lead to more effective fine-tuning and augmentation strategies. By implementing a combination of these approaches, it is possible to enhance the performance of LLMs on low-resource languages while maintaining their capabilities on high-resource languages.

What are the potential societal implications of linguistic discrimination in LLMs, and how can we address these issues to ensure equitable access to these technologies?

Linguistic discrimination in Large Language Models (LLMs) can have significant societal implications, including: Marginalization: LLMs that perform poorly on certain languages can marginalize speakers of those languages, limiting their access to information, services, and opportunities available through these technologies. Bias and Inequality: Linguistic discrimination can perpetuate biases and inequalities, favoring high-resource languages and communities while disadvantaging low-resource languages and marginalized groups. Cultural Preservation: LLMs that prioritize high-resource languages may contribute to the erosion of linguistic diversity and cultural heritage by neglecting languages with fewer resources and speakers. To address these issues and ensure equitable access to LLM technologies, the following steps can be taken: Diverse Training Data: Ensure that LLMs are trained on diverse datasets that include a wide range of languages, with a focus on balancing the representation of high and low-resource languages. This can help mitigate linguistic discrimination by providing the model with exposure to various linguistic contexts. Fine-Tuning and Augmentation: Implement fine-tuning and data augmentation techniques specifically tailored for low-resource languages to improve the model's performance on these languages. By enhancing the model's capabilities in underrepresented languages, we can reduce linguistic discrimination. Community Engagement: Engage with communities speaking low-resource languages to understand their needs and challenges. Collaborate with local experts and organizations to co-create solutions that address linguistic discrimination and promote inclusivity. Ethical Guidelines: Establish and adhere to ethical guidelines for the development and deployment of LLMs to ensure fair and unbiased treatment of all languages and communities. Transparency and accountability are essential in mitigating linguistic discrimination. By taking a proactive and inclusive approach to addressing linguistic discrimination in LLMs, we can promote equitable access to these technologies and foster linguistic diversity and inclusion.

Given the inherent limitations of LLMs, what alternative approaches or architectures could be explored to develop language models that are truly multilingual and unbiased?

To overcome the limitations of Large Language Models (LLMs) and develop language models that are truly multilingual and unbiased, several alternative approaches and architectures can be explored: Meta-Learning: Meta-learning techniques can be employed to enable language models to quickly adapt to new languages and tasks with minimal data. By leveraging meta-learning, models can generalize better across languages and exhibit improved multilingual capabilities. Ensemble Models: Utilizing ensemble models that combine multiple LLMs trained on different languages can enhance the overall performance and robustness of the language model. Ensemble methods can help mitigate biases and improve the model's multilingual capabilities. Zero-Shot Learning: Zero-shot learning approaches enable models to perform tasks in languages they have not been explicitly trained on. By incorporating zero-shot learning mechanisms, language models can exhibit better generalization and adaptability to new languages. Cross-Lingual Embeddings: Leveraging cross-lingual embeddings that map words and sentences from different languages into a shared semantic space can facilitate multilingual understanding and transfer learning. These embeddings can help bridge the gap between languages and improve the model's multilingual capabilities. Adversarial Training: Adversarial training techniques can be used to enhance the robustness and fairness of language models against linguistic discrimination. By training models to resist adversarial attacks and biases, we can promote unbiased and equitable language processing. Hybrid Models: Hybrid models that combine the strengths of symbolic and neural approaches can offer a more comprehensive understanding of languages and improve the model's ability to handle linguistic nuances and complexities. By exploring these alternative approaches and architectures, we can advance the development of language models that are truly multilingual, unbiased, and capable of promoting inclusivity and fairness in natural language processing tasks.