arXiv:2607. 19101v1 Announce Type: cross Abstract: Reliable Text Difficulty Assessment is a prerequisite for valid text simplification workflows and personalized learning applications.
arXiv:2608. 03803v1 Announce Type: cross Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a language rather than whether it commands the language itself, conflating fluency with proficiency.
arXiv:2609.01356v1 Announce Type: new Abstract: Multilingual large language models (mLLMs) achieve strong performance in machine translation, yet our understanding of the mechanisms by which they tra...
arXiv:2607. 19243v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages.
The paper proposes treating translation as a structured decision space explored by multiple autonomous agents, rather than producing a single output. Using Turkish–Syrian Arabic dialogue, three agents—zero‑shot, dialect‑stabilized, and pivot translation—are compared on 5,000 sentences, with stabilization nearly doubling dialect marker usage and reducing structural instability. The study introduces an interpretability framework that quantifies decision flexibility through dialect marker frequency, lexical proximity, and structural variance.
arXiv:2608.28776v1 Announce Type: new Abstract: Multilingual sentence embeddings are increasingly used to estimate semantic similarity across languages, yet their sensitivity to fine-grained translat...
arXiv:2510. 15551v2 Announce Type: replace-cross Abstract: Any piece of knowledge is usually expressed in one or a handful of natural languages on the web or in any large corpus.
The paper proposes the interlingua hypothesis, suggesting that large language models translate by encoding a source sentence into a latent, task‑agnostic feature space and then decoding a target sentence from that space. Three lines of evidence support this: (1) BLEU variance across language pairs is largely explained by language‑specific competences without pair‑specific interactions; (2) many model components influence both monolingual and translation tasks; and (3) fine‑tuning on monolingual data recovers most translation gains seen with aligned documents. These findings converge to support the hypothesis and point toward new ways to understand and improve LLM translation.
arXiv:2609.18156v1 Announce Type: new Abstract: Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating l...
arXiv:2609.15079v1 Announce Type: cross Abstract: Multi-agent LLM architectures, such as LangChain and AutoGen, largely assume English as the lingua franca for internal inter-agent communication, eve...
arXiv:2011.03783v3 Announce Type: replace-cross Abstract: In this work, we introduce the construction of a machine translation (MT) assisted and human-in-the-loop multilingual parallel corpus with an...
Teochew has a substantial speaker community and exhibits distinctive lexical, syntactic, and pragmatic features, yet textual resources for evaluating large language models remain limited. We present T...