CulturalBench: A Robust, Diverse and Challenging Benchmark for Measuring LMs' Cultural Knowledge Through Human-AI Red-Teaming (2025 · 38 citations)
Measuring how well language models understand everyday cultural practice worldwide is difficult. Static hand-written test sets soon become too easy. Benchmarks built from web pages, value surveys or synthetic generation may overlap with training data or carry over model biases.
Filtered Corpus Training (FiCT) Shows that Language Models Can Generalize from Indirect Evidence (2024 · 23 citations)
Do language models learn grammar by memorizing constructions they have seen, or can they infer rules for structures that never appear in their input? The authors propose Filtered Corpus Training (FiCT) to test this.
Language Matters: How Do Multilingual Input and Reasoning Paths Affect Large Reasoning Models? (2025 · 17 citations)
Large reasoning models (LRMs) write an explicit thinking trace before they answer. It is unclear which language that trace uses when the prompt is not in English, and whether the choice matters. Tam et al. show that open-weight LRMs such as QwQ-32B and Qwen3-30B-A3B mostly think in English, and sometimes in Chinese.…

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Updated 30 October 2026