Leveraging Open-Source Large Language Models for Native Language Identification

• URL: http://arxiv.org/abs/2409.09659v2
• Date: Sun, 19 Jan 2025 16:50:20 GMT
• Title: Leveraging Open-Source Large Language Models for Native Language Identification
• Authors: Yee Man Ng, Ilia Markov,
• Abstract summary: Native Language Identification (NLI) has applications in forensics, marketing, and second language acquisition. This study explores the potential of using open-source generative large language models (LLMs) for NLI.
• Score: 1.6267479602370543
• License:
• Abstract: Native Language Identification (NLI) - the task of identifying the native language (L1) of a person based on their writing in the second language (L2) - has applications in forensics, marketing, and second language acquisition. Historically, conventional machine learning approaches that heavily rely on extensive feature engineering have outperformed transformer-based language models on this task. Recently, closed-source generative large language models (LLMs), e.g., GPT-4, have demonstrated remarkable performance on NLI in a zero-shot setting, including promising results in open-set classification. However, closed-source LLMs have many disadvantages, such as high costs and undisclosed nature of training data. This study explores the potential of using open-source LLMs for NLI. Our results indicate that open-source LLMs do not reach the accuracy levels of closed-source LLMs when used out-of-the-box. However, when fine-tuned on labeled training data, open-source LLMs can achieve performance comparable to that of commercial LLMs.

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