arXiv:2605. 02608v2 Announce Type: replace-cross Abstract: Transformer-based models achieve state-of-the-art dependency parsing for high-resource languages, yet their advantage over simpler architectures in low-resource settings remains poorly understood.
By Kevin Guan, Happy Buzaaba, Christiane Fellbaum
arXiv:2606. 12708v1 Announce Type: cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP.
By Happy Buzaaba, Cheikh Mouhamadou Bamba Dione, David Ifeoluwa Adelani, Sylvain Kahane, Kim Gerdes, Bruno Guillaume, Kevin Guan, Aremu Anuoluwapo, Naome A. Etori, Shamsuddeen Hassan Muhammad, Utitofon Inyang, Peter Nabende, David Sabiiti Bamutura, Andiswa Bukula, Chinedu Uchechukwu, Rooweither Mabuya, Idris Akinade, Christiane Fellbaum
FreqBLiMP is a frequency‑controlled extension of the BLiMP minimal‑pair benchmark that regenerates all 67 paradigms under explicit Zipf‑frequency regimes while preserving grammatical contrasts. The study evaluates multiple open‑weight LLM families and finds that lower lexical frequency consistently reduces sentence likelihood, yet overall contrastive acceptability accuracy drops only modestly. However, the stability in aggregate accuracy hides significant variability across linguistic phenomena, with models remaining robust on overt morphosyntactic generalization but degrading on lemma‑specific tasks.
By Tyrone White, Yuki Arase
Preference optimisation has proven effective for improving large language models but typically relies on costly human preference annotations. Extending these methods to morphologically rich, low-resource languages remains challenging because such annotations are scarce.
arXiv:2608. 06179v1 Announce Type: new Abstract: Preference optimisation has proven effective for improving large language models but typically relies on costly human preference annotations.
By Hoda Fakharzadehjahromy, Emil Wiman, Andreas Bueff, Hafsteinn Einarsson, Fredrik Heintz
The paper investigates how to fairly compare language models across languages, noting that current evaluation methods vary widely and lack empirical validation. By training controlled monolingual models on parallel data and testing multilingual LLMs, the authors find that many normalized metrics suffer from biases due to tokenization, encoding, and orthographic differences. Instead, they recommend using sentence‑level negative log‑likelihood over semantically equivalent sequences for more reliable cross‑lingual comparisons.
By Xiulin Yang, Ethan Gotlieb Wilcox, Catherine Arnett