The paper introduces the first reproducible taxonomy for programming languages based on resource availability, categorizing 646 languages into four tiers. It finds that a small fraction (1.9%) of high-resource languages (Tier 3) generate the majority (74.6%) of tokens in major corpora, while the majority of languages (71.7%) are scarce and contribute only 1.0% of tokens. Statistical analysis confirms the extreme and systematic imbalance across tiers.
arXiv:2603. 14501v2 Announce Type: replace-cross Abstract: Large Language Models excel in high-resource programming languages but struggle with low-resource ones.
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.
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.
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.
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.
arXiv:2508. 16131v3 Announce Type: replace-cross Abstract: Code completion entails the task of providing missing tokens given a surrounding context.
arXiv:2604.16656v2 Announce Type: replace Abstract: All languages are equal; when it comes to tokenization, some are more equal than others. Tokens are the hidden currency that dictates the cost and...
The paper introduces AssameseLM-E2B, a 2‑billion‑parameter language model tailored for the low‑resource Assamese language, and its instruction‑tuned variant AssameseLM-E2B‑it. Built on the Gemma‑4 architecture, it uses an expanded byte‑level BPE vocabulary that cuts tokenization overhead by nearly 500 % compared with multilingual baselines, and employs a parameter‑efficient Continual Pre‑Training pipeline with Hybrid LoRA followed by supervised fine‑tuning on a balanced, multi‑domain dataset. Evaluation shows strong performance with a Bits‑per‑Byte score of 0.7488 on FLORES‑200, a 96.7 % RAG score, and 100 % refusal accuracy on adversarial prompts, highlighting the effectiveness of parameter‑efficient adaptation for locally deployable models under constrained resources.
The paper audits 51 recent multilingual evaluation benchmarks covering 242 datasets and 219 languages, focusing on coverage, representativeness, and rigor. It finds that while coverage is broad, many languages appear in only one benchmark and entire regions are underrepresented; low‑resource languages face a task equity gap. Representativeness is compromised by heavy reliance on English translations, and rigor is weakened by benchmark contamination and parallel‑corpus overlap. The authors propose concrete recommendations for building evaluation frameworks that are natively constructed, culturally grounded, contamination‑aware, and community‑aligned.
The paper introduces CodeGraph, an open‑taxonomy knowledge graph that semantically annotates source code by extracting entities such as algorithms, paradigms, design patterns, and application domains from millions of files. Using a specialized large language model and a three‑stage Wikidata linking process, the authors ground these entities in Wikidata and construct a graph with about 158 million nodes and 1 billion typed edges across 14 programming languages. A quality‑assurance protocol combining human evaluation and an LLM‑as‑a‑judge filter quantifies annotation precision.
The paper audits 51 multilingual evaluation benchmarks covering 242 datasets and 219 languages, focusing on coverage, representativeness, and rigor. It finds that while coverage is broad, many languages appear in only one benchmark and entire regions are underrepresented, with low-resource languages evaluated on far fewer tasks than high-resource ones. The study also highlights that most datasets rely on English translations, creating artifacts and limiting cultural relevance, and that benchmark contamination through translation leakage and corpus overlap undermines rigor. Recommendations call for natively constructed, culturally grounded, contamination-aware evaluation frameworks that truly serve the communities they aim to assess.
arXiv:2610.08794v1 Announce Type: new Abstract: Large language models rely on subword tokenizers whose quality varies across languages, yet no standardized multi-metric framework exists for broad com...