Evaluating Large Language Model Performance on Part-of-Speech Tagging for Indigenous and Low-resource Brazilian Languages

Core Concepts

Large Language Models perform poorly on part-of-speech tagging for indigenous and low-resource Brazilian languages compared to high-resource languages, but language adaptation can improve cross-lingual transfer performance.

Abstract

The study evaluates the performance of Large Language Models (LLMs) like GPT-4 and cross-lingual transfer using XLM-R on part-of-speech (POS) tagging for 12 low-resource Brazilian languages, 2 low-resource African languages, and 2 high-resource languages (English and Brazilian Portuguese). The key findings are: LLMs perform worse on POS tagging for low-resource languages (less than 34% accuracy) compared to high-resource languages (over 90% accuracy). GPT-4 performs slightly better than zero-shot cross-lingual transfer from English and Portuguese using XLM-R, indicating better abilities of LLMs for this task. Language adaptive fine-tuning (LAFT) of XLM-R on the limited Bible corpus for 7 out of the 12 Brazilian languages can improve the cross-lingual transfer performance by 3 to 12 percentage points on 6 out of the 7 languages. The authors provide error analysis examples showing that LLMs struggle with POS tagging due to similarities between low-resource language words and English words. The study highlights the need for building more NLP resources across different tasks for these low-resource languages to improve performance.

Stats

POS tagging accuracy for English and Portuguese is over 90% when training data is available. POS tagging accuracy for low-resource Brazilian languages is less than 34% using zero-shot cross-lingual transfer. Language adaptive fine-tuning can improve cross-lingual transfer performance by 3 to 12 percentage points on 6 out of 7 Brazilian languages.

Quotes

"Our results indicate low performance (less than 34.0% while high-resource languages achieved over 90.0%)." "GPT-4 leads to better results and Brazilian Portuguese performs better than English in zero-shot evaluation." "Language adaptation using XLM-R on each language, before fine-tuning Brazilian Portuguese, and evaluating on that language boosts the performance by +3 to +12.0 points on six out of seven languages."

Key Insights Distilled From

Comparing LLM prompting with Cross-lingual transfer performance on Indigenous and Low-resource Brazilian Languages

by Davi... at arxiv.org 04-30-2024

https://arxiv.org/pdf/2404.18286.pdf

Deeper Inquiries

What other NLP tasks beyond POS tagging could be evaluated for these low-resource Brazilian languages to better understand the capabilities and limitations of Large Language Models?

In addition to POS tagging, several other NLP tasks could be evaluated for low-resource Brazilian languages to gain a comprehensive understanding of the capabilities and limitations of Large Language Models (LLMs). Some of these tasks include: Named Entity Recognition (NER): Evaluating the performance of LLMs in identifying and classifying named entities in text can provide insights into their ability to understand the context and identify important entities such as names of people, organizations, and locations. Sentiment Analysis: Assessing how well LLMs can analyze and classify the sentiment expressed in text can be valuable for applications like social media monitoring, customer feedback analysis, and opinion mining in low-resource languages. Machine Translation: Evaluating the LLMs' performance in translating text between low-resource Brazilian languages and more widely spoken languages can shed light on their effectiveness in handling translation tasks for under-resourced languages. Text Summarization: Understanding how well LLMs can generate concise and informative summaries of longer texts in low-resource languages can be beneficial for tasks like document summarization and information retrieval. Named Entity Linking (NEL): Assessing the ability of LLMs to link named entities mentioned in text to knowledge bases or external resources can enhance information retrieval and knowledge extraction tasks in these languages. By evaluating LLMs on a diverse set of NLP tasks beyond POS tagging, researchers can gain a holistic view of their performance and identify areas for improvement in handling various linguistic challenges present in low-resource Brazilian languages.

How could the language adaptation approach be further improved to achieve higher performance gains for cross-lingual transfer to low-resource languages with extremely limited data?

To enhance the language adaptation approach for achieving higher performance gains in cross-lingual transfer to low-resource languages with limited data, several strategies can be considered: Data Augmentation: Implementing data augmentation techniques such as back-translation, paraphrasing, and synthetic data generation can help increase the diversity and size of the training data, thereby improving the model's ability to generalize to unseen languages. Transfer Learning from Related Languages: Leveraging transfer learning from related languages or language families can provide a useful initialization point for adapting the model to low-resource languages, enabling faster convergence and better performance. Fine-tuning Strategies: Exploring different fine-tuning strategies, such as layer freezing, gradual unfreezing, and differential learning rates, can help optimize the adaptation process and improve the model's performance on specific linguistic characteristics of low-resource languages. Task-Specific Adaptation: Tailoring the adaptation process to specific NLP tasks prevalent in low-resource languages, such as morphological analysis, syntactic parsing, or semantic role labeling, can lead to task-specific improvements in model performance. Ensemble Methods: Combining multiple adapted models through ensemble methods can help mitigate individual model biases and errors, leading to more robust and accurate predictions for cross-lingual transfer tasks. By incorporating these strategies into the language adaptation approach, researchers can enhance the performance of LLMs in cross-lingual transfer to low-resource languages with limited data, ultimately improving their utility in diverse linguistic contexts.

Given the diversity of indigenous languages in Brazil, what innovative approaches could be explored to build comprehensive NLP resources and technologies that can truly serve and empower these language communities?

To build comprehensive NLP resources and technologies that cater to the diverse indigenous languages in Brazil and empower these language communities, several innovative approaches can be explored: Community-Centric Data Collection: Engaging with indigenous communities to collect and annotate linguistic data in their native languages, ensuring cultural sensitivity and community involvement in the resource creation process. Crowdsourcing and Citizen Science: Leveraging crowdsourcing platforms and citizen science initiatives to gather linguistic data, annotate corpora, and co-create NLP resources with indigenous language speakers and experts. Multimodal Data Integration: Integrating multimodal data sources, including audio, video, and text, to capture the richness and complexity of indigenous languages, enabling the development of multimodal NLP models for these languages. Zero-Shot Learning and Few-Shot Learning: Exploring zero-shot and few-shot learning techniques to adapt pre-trained models to new indigenous languages with limited data, facilitating rapid deployment of NLP technologies for under-resourced languages. Ethical AI Guidelines: Incorporating ethical AI principles and guidelines in the development of NLP resources for indigenous languages, ensuring data privacy, cultural preservation, and respectful representation of indigenous knowledge and traditions. Partnerships and Collaborations: Establishing partnerships with indigenous language advocates, academic institutions, and technology companies to co-design and implement NLP initiatives that prioritize the needs and aspirations of indigenous communities. By embracing these innovative approaches and fostering collaborative efforts, it is possible to build inclusive and sustainable NLP resources and technologies that not only preserve the linguistic heritage of indigenous languages in Brazil but also empower these communities to participate in the digital age on their own terms.