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Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions (2402.18025v2)

Published 28 Feb 2024 in cs.CL

Abstract: How can LLMs process and translate endangered languages? Many languages lack a large corpus to train a decent LLM; therefore existing LLMs rarely perform well in unseen, endangered languages. On the contrary, we observe that 2000 endangered languages, though without a large corpus, have a grammar book or a dictionary. We propose LINGOLLM, a training-free approach to enable an LLM to process unseen languages that hardly occur in its pre-training. Our key insight is to demonstrate linguistic knowledge of an unseen language in an LLM's prompt, including a dictionary, a grammar book, and morphologically analyzed input text. We implement LINGOLLM on top of two models, GPT-4 and Mixtral, and evaluate their performance on 5 tasks across 8 endangered or low-resource languages. Our results show that LINGOLLM elevates translation capability from GPT-4's 0 to 10.5 BLEU for 10 language directions. Our findings demonstrate the tremendous value of linguistic knowledge in the age of LLMs for endangered languages. Our data, code, and model generations can be found at https://github.com/LLiLab/LLM4endangeredlang.

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Citations (14)
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Summary

  • The paper presents LingoLLM, which uses in-context linguistic descriptions as a supervised signal to improve learning for endangered and low-resource languages.
  • It was empirically evaluated on eight languages, with detailed tests on Manchu showing enhanced accuracy in language comprehension tasks.
  • The study discusses expanding the method to non-Romanized scripts and broader contexts, offering scalable solutions for language preservation.

Review of "Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions"

The paper "Hire a Linguist!: Learning Endangered Languages with In-Context Linguistic Descriptions," authored by researchers from Carnegie Mellon University and UC Santa Barbara, presents an innovative approach aimed at addressing the challenges associated with learning endangered and low-resource languages. The work is structured around the newly introduced method termed LingoLLM, which leverages in-context linguistic descriptions to enhance language learning.

Summary of LingoLLM

LingoLLM builds on existing research in natural language processing and machine learning to create models that can learn languages with scant data resources. The authors highlight the method's foundation in contextual learning, which allows it to make practical use of linguistic descriptions during the training process. This is particularly relevant for endangered languages, which often lack extensive training corpora.

Their approach is unique as it facilitates the acquisition of language knowledge by using linguistic context as a form of supervised learning signal. By incorporating descriptions that linguists might provide, the model can better infer language rules and nuances that might otherwise be absent in limited datasets.

Experimental Evaluation

An empirical analysis was conducted on eight low-resource and endangered languages. Though the paper primarily focuses on Manchu, the results provide a compelling case for the feasibility of LingoLLM in aiding the linguistic comprehension of these languages. The paper notes, however, that the evaluation of certain tasks such as math reasoning, keyword-to-text generation, and word reordering was confined to Manchu, suggesting the need for broader testing across other languages.

A key finding is the model's effectiveness when linguistic information is present, demonstrating improved accuracy in language comprehension tasks compared to traditional methods. However, the paper acknowledges potential limitations due to the narrow scope of languages tested and the constraints of the Romanized script, which are prevalent in the current version of LingoLLM.

Analytical Discussion

The authors provide a critical discussion on how the LingoLLM method could be further extended to support a wider variety of linguistic contexts. They propose that future research could expand the model's applicability to languages without a Romanized script, which would increase its utility for many unrepresented language communities. The discussion suggests potential pathways for enhancing the model’s robustness and efficacy in real-world applications.

Implications and Future Directions

The implications of LingoLLM are noteworthy in both practical and theoretical contexts. Practically, the method offers a scalable solution for language preservation efforts, potentially aiding in the documentation and revitalization of endangered languages. Theoretically, it opens avenues for exploring the integration of linguistic knowledge in machine learning algorithms, promoting more intelligent and culturally enriching interactions with AI systems.

Speculatively, future developments could involve cross-disciplinary collaborations involving linguists, technologists, and computational researchers to further advance the model. Integration with broader multilingual NLP systems might also lead to significant improvements in low-resource language processing capabilities.

In conclusion, this paper provides a robust contribution to the field of computational linguistics and natural language processing by introducing a novel method with promising potential for impact on endangered language learning and preservation.

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