Leveraging Open-Source Large Language Models for Native Language Identification
<|reference_start|>Leveraging Open-Source Large Language Models for Native Language Identification: 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.<|reference_end|>
@article{ng2024leveraging,
    title={Leveraging Open-Source Large Language Models for Native Language
  Identification},
    author={Yee Man Ng and Ilia Markov},
    journal={arXiv preprint arXiv:2409.09659},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09659},
    primaryClass={cs.CL}
}