Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases

This study evaluates Large Language Models' (LLMs) ability to simulate non-native-like English use observed in human second language (L2) learners interfered with by their native first language (L1). In dialogue-based interviews, we prompt LLMs to mimic L2 English learners with specific L1s (e.g., Japanese, Thai, Urdu) across seven languages, comparing their outputs to real L2 learner data. Our analysis examines L1-driven linguistic biases, such as reference word usage and avoidance behaviors, using information-theoretic and distributional density measures. Results show that modern LLMs (e.g., Qwen2.5, LLAMA3.3, DeepseekV3, GPT-4o) replicate L1-dependent patterns observed in human L2 data, with distinct influences from various languages (e.g., Japanese, Korean, and Mandarin significantly affect tense agreement, and Urdu influences noun-verb collocations). Our results reveal the potential of LLMs for L2 dialogue generation and evaluation for future educational applications.

CNIMA: A Universal Evaluation Framework and Automated Approach for Assessing Second Language Dialogues

We develop CNIMA (Chinese Non-Native Interactivity Measurement and Automation), a Chinese-as-a-second-language labelled dataset with 10K dialogues. We annotate CNIMA using an evaluation framework -- originally introduced for English-as-a-second-language dialogues -- that assesses micro-level features (e.g.\ backchannels) and macro-level interactivity labels (e.g.\ topic management) and test the framework's transferability from English to Chinese. We found the framework robust across languages and revealed universal and language-specific relationships between micro-level and macro-level features. Next, we propose an approach to automate the evaluation and find strong performance, creating a new tool for automated second language assessment. Our system can be adapted to other languages easily as it uses large language models and as such does not require large-scale annotated training data.

Interaction Matters: An Evaluation Framework for Interactive Dialogue Assessment on English Second Language Conversations

We present an evaluation framework for interactive dialogue assessment in the context of English as a Second Language (ESL) speakers. Our framework collects dialogue-level interactivity labels (e.g., topic management; 4 labels in total) and micro-level span features (e.g., backchannels; 17 features in total). Given our annotated data, we study how the micro-level features influence the (higher level) interactivity quality of ESL dialogues by constructing various machine learning-based models. Our results demonstrate that certain micro-level features strongly correlate with interactivity quality, like reference word (e.g., she, her, he), revealing new insights about the interaction between higher-level dialogue quality and lower-level linguistic signals. Our framework also provides a means to assess ESL communication, which is useful for language assessment.