Inferring L1 Backgrounds from L2 English with LLM Token Probabilities
Native language identification (NLI) infers a non-native speaker's first language (L1) from their second language (L2) production.
ProofPaper ↗
Key points
- We investigate a token probability approach that scores each text sample as a continuation of a prompt prefix specifying a candidate L1, then predicts the candidate with the highest score.
- We evaluate our approach using Llama 3.1 8B and 70B on three datasets: TOEFL9, Write and Improve 2024, and BULATS-L1.
- We compare its performance with previous prompting-based classification approaches.
- Our results indicate that pretrained LLMs' token probabilities contain information useful for distinguishing L1 backgrounds from L2 English production.
Sources (1)
- [1]Inferring L1 Backgrounds from L2 English with LLM Token ProbabilitiesarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 11:12 AM
Native language identification (NLI) infers a non-native speaker's first language (L1) from their second language (L2) production.
We investigate a token probability approach that scores each text sample as a continuation of a prompt prefix specifying a candidate L1, then predicts the candidate with the highest score.
Extractive summary: sentences quoted from the sources.
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