While LLMs offer promising capabilities for endangered language documentation, several limitations and drawbacks should be considered:
Data Scarcity: Endangered languages often lack sufficient annotated data, which poses a significant challenge for LLMs that typically require large datasets for effective training. The limited availability of high-quality training data can hinder the model's ability to learn the unique linguistic features of these languages.
Cultural Sensitivity and Context: Language is deeply intertwined with culture, and LLMs may not fully grasp the cultural nuances and contextual meanings inherent in endangered languages. This lack of cultural understanding can lead to inaccuracies in glossing and translation, potentially misrepresenting the language and its speakers.
Dependence on Training Data: LLMs are trained on vast corpora that may not include sufficient representation of endangered languages. Consequently, their performance may be biased towards more prevalent languages, leading to suboptimal results for low-resource languages.
Lack of Explainability: LLMs often operate as "black boxes," making it difficult to understand how they arrive at specific outputs. This lack of transparency can be problematic in linguistic documentation, where understanding the rationale behind glossing decisions is crucial for accuracy and reliability.
Environmental Impact: The use of large language models entails significant computational resources, which can have a high environmental cost. This raises ethical concerns, particularly when working with endangered languages that are often tied to marginalized communities.
Potential for Misuse: Relying solely on automated systems for language documentation may lead to the undervaluation of human expertise. There is a risk that automated glossing could replace human annotators, undermining the quality and depth of linguistic analysis that skilled linguists provide.
Limited Adaptability: LLMs may struggle to adapt to the evolving nature of languages, particularly endangered ones that may be undergoing revitalization efforts. Their static training data may not reflect recent changes or developments in the language.
These limitations highlight the need for a balanced approach that combines LLM capabilities with human expertise and cultural sensitivity in the documentation of endangered languages.