Inferring L1 Backgrounds from L2 English with LLM Token Probabilities

The paper presents a method for inferring a speaker’s first language (L1) from their second‑language English (L2) by using token probability scores from large language models (LLMs). By treating each text as a continuation of a prompt that specifies a candidate L1, the authors identify the L1 with the highest probability score. Experiments with Llama 3.1 8B and 70B on TOEFL9, Write and Improve 2024, and BULATS‑L1 datasets show that token‑probability scoring outperforms direct classification, with the 70B model achieving state‑of‑the‑art results across all datasets.

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• llms
• benchmarks