Users frequently express their beliefs to large language models (LLMs).In some situations, it is ideal for the LLM to accept this contextual information as true, while in others, it is ideal to stick to prior knowledge.Users' expressions of belief (EoBs) can take linguistically diverse forms-using presuppositions, evidential and certainty markers, or varied toneseach of which may have a different persuasiveness over the LLMs.We introduce a benchmark to systematically evaluate how different EoBs affect whether models follow context versus prior knowledge.We propose a typology grounded in four linguistically motivated dimensions: form, evidentiality, epistemic stance, and tone, spanning 19 fine-grained types.By pairing these EoBs with world knowledge facts, we generate controlled EoB-query pairs that isolate the effect of linguistic variation.We use our benchmark to evaluate 18 LLMs that differ in architecture (Llama3, Qwen3, Gemma3), scale (1B-30B parameters), and training stages (base vs instruct).We identify meaningful variations in response behavior across these axes: For example, bigger models and instruction models tend to be less context-following than smaller models and base models.We further identify specific EoBs that statistically significantly persuade LMs more consistently than others.These systematic patterns in how linguistic framing affects LLM context integration serve to evaluate model robustness and inform best practices for prompt engineering.We publicly release code and data used in this project.

Recognizing semantic differences across documents is crucial for text generation evaluation and content alignment, especially in crosslingual settings.However, as a standalone task, it has received little attention.We address this by introducing SwissGov-RSD, the first naturalistic, document-level, cross-lingual dataset for semantic difference recognition.It encompasses a total of 224 multi-parallel documents in English-German, English-French, and English-Italian with token-level difference annotations by human annotators.We evaluate a variety of open-source and closed-source large language models as well as encoder models across different fine-tuning settings on this new benchmark.Our results show that current automatic approaches perform poorly compared to their performance on monolingual, sentence-level, and synthetic benchmarks, revealing a considerable gap for both LLMs and encoder models.We make our code and dataset publicly available 1 .