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.
arXiv:2610.32382v1 Announce Type: new Abstract: Spoken native language (L1) identification for second language learners has traditionally relied on acoustic attributes of speech. Comparatively little...
arXiv:2610.16601v1 Announce Type: cross Abstract: Processing multilingual text requires both identifying a broad range of languages and distinguishing the language of each token in inputs containing...
arXiv:2504.11952v4 Announce Type: replace-cross Abstract: An ideal detection system for machine generated content is supposed to work well on any generator as many more advanced LLMs come into existe...
arXiv:2609.10122v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance across a broad range of classification settings, yet the reliability of their predictions...
arXiv:2512. 20757v2 Announce Type: replace-cross Abstract: Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs).
arXiv:2607. 21458v1 Announce Type: new Abstract: The rise of human-AI collaborative writing has created a growing need for fine-grained detection methods that support localizing likely LLM-generated content in mixed-authorship documents.
arXiv:2608.03446v2 Announce Type: replace Abstract: Multilingual large language models (LLMs) have been shown to perform better on non-English classification tasks when the representations of the giv...
AudioLLMs enable speech recognition conditioned on textual prompts such as domain descriptions or entity lists. However, it remains unclear whether these models genuinely utilise such context or rely on parametric knowledge learned during pretraining.
arXiv:2604.08568v3 Announce Type: replace-cross Abstract: The widespread use of LLM-based writing assistance has raised an interesting question about the homogenization of English. As LLMs tend to re...
The paper investigates whether intrinsic tokenizer features can predict the performance of downstream language models. By training about 200 models across nine languages and evaluating numerous tokenizer metrics, the authors find that none of the existing metrics consistently forecasts model performance across different families, tasks, and languages. They propose a new linguistically grounded metric that captures aspects of tokenizer quality missed by prior measures, though its predictive power may still vary.
The paper introduces a token‑level extension of Omni‑Temporal Classification (OTC) for automatic speech recognition, allowing unsupported tokens to be bypassed while preserving supervision for the rest of the word. Across 19 languages and three corpora, this token‑level OTC consistently outperforms standard CTC, achieving the lowest mean word error rate on every dataset and a 9.45% average relative WER reduction. A predictive‑entropy‑indexed schedule replaces epoch‑based relaxation, reducing training‑length dependence while maintaining performance.
arXiv:2609.36194v1 Announce Type: new Abstract: Extracted sentiment directions can vary across samples even when downstream sentiment classification remains accurate. To evaluate direction reproducib...