Core Concepts
LINGOLLM introduces a novel approach to enable large language models to process and translate endangered languages by integrating linguistic descriptions. The core thesis is that leveraging linguistic knowledge can significantly enhance the performance of language models on unseen and low-resource languages.
Abstract
LINGOLLM proposes a training-free approach to empower large language models to process and translate endangered languages by incorporating linguistic descriptions such as grammar books and dictionaries. The results demonstrate significant improvements in translation capability, response selection accuracy, mathematical reasoning, word reordering, and keyword-to-text tasks across multiple endangered or low-resource languages. By leveraging existing linguistic resources, LINGOLLM aims to bridge the gap between advanced language models and underrepresented languages, contributing to linguistic preservation and inclusivity in the digital age.
The content discusses the challenges faced in processing endangered languages due to limited training data availability for large language models. It highlights the importance of linguistic descriptions such as grammar books and dictionaries in enhancing the performance of language models on unseen languages. The proposed LINGOLLM approach integrates these linguistic resources effectively to improve translation quality, response selection accuracy, mathematical reasoning capabilities, and sentence structure understanding across diverse endangered languages.
LINGOLLM's success lies in its ability to leverage existing linguistic resources for endangered languages, enabling more accurate translations and better understanding of discourse. By utilizing grammar books, dictionaries, and morphological analyzers, LINGOLLM significantly enhances the performance of large language models on various NLP tasks for low-resource languages. This innovative approach not only improves communication but also contributes to preserving linguistic diversity in the digital era.
Stats
Our results show that LINGOLLM elevates translation capability from GPT-4’s 0 to 10.5 BLEU for 10 language directions.
LINGOLLM improves GPT-4’s mathematical reasoning accuracy from 18% to 75%.
Response Selection (Acc.) improved from 43% to 63% with LINGOLLM.
Translation quality increases from an incomprehensible 0.5 to 10 BLEU points with LINGOLLM.
Zero-shot GPT-4 has BLEU smaller than 1 for most languages except Wolof directions.
Quotes
"Many endangered languages lack extensive corpora but have valuable grammar books or dictionaries."
"Leveraging linguistic knowledge can significantly enhance large language models' performance on unseen languages."
"By integrating existing linguistic resources like grammar books and dictionaries, LINGOLLM bridges the gap between advanced AI technologies and underrepresented languages."