XDecideBench is a benchmark comprising nine suites across 122 languages, containing 88,425 texts and 153,875 typed questions, designed to evaluate Typed Decision Models and their confidence policies. The benchmark reports results separately for aligned, locally labelled, and native items, revealing that model rankings vary by suite and language, with the best English model performing poorly in many other languages. Fine‑tuned open models outperform their base Qwen counterparts in all twelve development languages, and confidence thresholds set on English data can exceed acceptable error rates on other datasets such as XNLI.
arXiv:2607. 20526v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings where fluent but incorrect answers can be costly.
The study evaluates whether a single confidence threshold in typed decision interfaces treats all languages equally. Using 1,000 items in 51 languages across three vendors, it finds that English consistently achieves higher coverage—clearing the 0.9 threshold 8.6–11.7 percentage points more often than other languages—despite similar accuracy. Even when thresholds are re‑weighted or calibrated per language, the gap persists, and translating inputs into English reduces but does not eliminate it. The authors recommend reporting per‑language coverage at the operating threshold.
Zuse is a family of decision models (4B, 9B, and 27B) that predict probabilities for a fixed set of options across 27 European languages by training a low‑rank adapter and a pointer head on frozen open‑weight bases. The 27B model achieves 87.5 % accuracy on a broad European test suite of 11 sources, 9 task groups, and 27 languages, with a pooled expected calibration error of 0.016. Post‑hoc analysis reveals that miscalibration is task‑dependent, that automated decision share aligns more with chance‑corrected skill than raw accuracy, and that performance on languages outside the base model’s documented list is consistently lower.
arXiv:2610.17205v1 Announce Type: new Abstract: LLM-based decision-making systems, also called System One models, have recently gained popularity, most notably through Jev (Almeida, 2026). Given a ty...
SyntaxBench is a diagnostic benchmark and statistical evaluation framework for character‑level reasoning in large language models, comprising five core tasks—character counting, letter containment, palindrome detection, edit distance, and longest‑string selection—and a harder substring‑extraction stress test called index_to_span. The benchmark uses paired English and random‑string inputs, zero‑, one‑, and four‑shot prompts, and evaluates models from 2B to 32B parameters across multiple reasoning modes. It reports a wide range of metrics, including exact‑match and relaxed accuracy, Cohen’s kappa, McNemar tests, bootstrap confidence intervals, Kendall’s tau, class‑conditional metrics, tokenization analysis, and multiple‑comparison‑corrected tests.
The paper investigates position bias in large language models (LLMs) across a wide range of model sizes—from billions to trillions of parameters—and across five languages. It finds that while small-scale models and non‑English languages still exhibit noticeable position bias, larger models and the use of Chain‑of‑Thought prompting substantially reduce this bias. Additionally, the study shows that emerging thinking models with advanced reasoning capabilities are almost immune to position bias, indicating a promising but resource‑intensive path to mitigating this vulnerability.
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: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:2610.19185v1 Announce Type: new Abstract: Evaluation of large language models (LLMs) remains largely pre-scientific: it involves many design decisions, with little guidance on how to make them....
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...
TypedBench is a new benchmark designed to evaluate system‑one decision models that output calibrated probabilities for typed answers—categorical, ordinal, or binary—using a non‑generative interface. The benchmark assesses models on policy adherence, wording robustness, probability quality (via selective prediction, ordinal proper scoring rules, and calibration error), and the cost of decisions under asymmetric cost matrices, using seven policy‑labelled generators and nine evaluation suites. Experiments on a hosted model, an open encoder, and a range of open decoders (0.8B–9B parameters) reveal that the hosted model is wording‑sensitive and underconfident, while decoders are slow and less accurate on policy questions, highlighting the need for joint evaluation across multiple dimensions.
arXiv:2504.18346v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been transformative across many domains. However, hallucination, i.e., confidently outputting incorrect inf...