arXiv:2402. 18121v2 Announce Type: replace-cross Abstract: This study assesses four cutting-edge language models in the underexplored Aminoacian language.
arXiv:2609.18284v1 Announce Type: new Abstract: In recent years, three initiatives have emerged to develop generative language models in Hungary. The motivation behind them is the same. For Hungarian...
The paper presents a pipeline that leverages large language models to extract grammatical rules, example sentences, and lexicons from descriptive grammar books, producing synthetic parallel corpora for fine‑tuning machine translation models. Evaluated on three low‑resource languages—Kalamang, Tuatschin, and Mandan—the synthetic data improves translation quality over seed‑data baselines in 75% of configurations for Kalamang and 59% for Tuatschin, achieving up to +8.8 ChrF++ gains. A factorial study across 96 configurations identifies which combinations of target part‑of‑speech, retrieval granularity, and sample volume drive performance gains and where they fail, demonstrating that static linguistic documentation can be repurposed for practical translation tools for severely under‑resourced languages.
arXiv:2601.19926v3 Announce Type: replace-cross Abstract: We present a systematic review of 337 articles evaluating the syntactic abilities of Transformer-based language models (TLMs), reporting on o...
The paper investigates how machine‑translated English data from 24 diverse source languages influences small English language models. It finds that source language affects model behavior: lexical diversity drives overall perplexity, while grammatical performance correlates with typological similarity to English when sufficient data is used. Additionally, translation quality strongly predicts language‑modeling performance.
The paper introduces the first large‑scale benchmark dataset of Bangla idioms, along with a synthetic multiple‑choice question set for idiom meaning identification. It evaluates recent large language models on three idiom‑related tasks—paraphrasing, idiom span detection, and meaning identification—using zero‑shot and few‑shot prompting. Results show significant variability across models, with Phi‑4‑mini‑instruct best at paraphrasing, Kimi‑K2‑32b‑instruct excelling at span detection, and Gemini‑2.5‑flash leading in meaning identification.
arXiv:2604. 18347v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) achieved rapid progress in the recent years.
The paper introduces NinaXander, a method for composing frozen language models from different architecture families by inserting a trained shared‑latent adapter between their layers. By running the initial layers of one model, converting the intermediate representation with the adapter, and then continuing with the remaining layers of another model, multiple composed models can be created without retraining. Experiments with RWKV and Pythia show that while some compositions preserve syntactic quality and reduce memory usage, none match the parent model’s accuracy and language‑modeling performance drops on out‑of‑domain data.
arXiv:2608.29921v1 Announce Type: cross Abstract: The output of a Language Model can be tampered with \emph{while} the model is writing it. A simple test can thus be constructed by evaluating the mod...
arXiv:2609.16967v1 Announce Type: new Abstract: Ensuring that multilingual language models generate coherent text in a specific target language is a major issue in multilingual language modeling. We...
While large language models have been dominating the research landscape recently, small language models remain highly relevant across various domains; yet, they receive far less attention. In this study, we investigate how smaller language models perform during the generation stage within a Retrieval-Augmented Generation (RAG) system.
arXiv:2608. 07629v1 Announce Type: cross Abstract: Multilingual neural machine translation models such as NLLB-200 cover 200 languages but leave thousands unsupported, including most Grassfields Bantu languages of Cameroon.
arXiv:2606. 02147v1 Announce Type: cross Abstract: Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation.