arXiv:2603. 14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones.
By Junhang Cheng, Fang Liu, Jia Li, Chengru Wu, Nanxiang Jiang, Li Zhang
arXiv:2606. 08840v1 Announce Type: new Abstract: Code generation models are typically compared using compact execution benchmarks and aggregate pass rates, but such summaries obscure how performance varies across programming languages, problem families, and failure modes.
By Sayed Erfan Arefin
arXiv:2605. 00754v4 Announce Type: replace-cross Abstract: Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling.
By Indraneil Paul, Goran Glava\v{s}, Iryna Gurevych
The paper investigates the energy costs of multilingual large language model (LLM) inference, revealing significant disparities across languages. Using the ML.Energy framework, the authors find that energy consumption per output token can differ by up to 8.3×, and total energy for a fixed request set can vary up to 179×, with English being the cheapest and Pashto the most expensive. The study attributes these differences to higher per-token costs for complex or rare scripts and longer outputs for low‑resource languages, and notes that high‑energy languages also tend to have lower task accuracy.
By Naihao Deng, Alissa Shen, Yiming Feng, Joan Nwatu, Jae-Won Chung, Mosharaf Chowdhury, Yulong Chen, Rada Mihalcea
arXiv:2608. 15964v1 Announce Type: cross Abstract: Language-specific competency (LSC) is the phenomenon of a language model performing better or worse depending on the language of the prompt.
By Ishika Agarwal, Arkajyoti Charaborty, Tanner Sorensen, Neha Gupta, Andreas Stolcke
arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.
By Zoe Kotti, Konstantina Dritsa, Diomidis Spinellis, Panos Louridas