Linear probes can recover deception-related signals from large language model activations, but existing evidence is overwhelmingly English-centric. We investigate whether this discrimination transfers across languages and what representational geometry accompanies such transfer. We train instructed-deception probes on controlled honest/deceptive instruction contrasts in English, Portuguese, Spanish, German, and Chinese, and evaluate them without target-language refitting on a multilingual adaptation of DeceptionBench across five models spanning four different families. Cross-language performance remains close to matched-language baselines, with all models satisfying our prespecified noninferiority criterion, although absolute discrimination varies substantially across models. We also find that language-specific probe directions are strongly aligned and concentrate around a shared consensus direction, while response representations retain clear language-specific organization. Additional construct-validity analyses show that measurable response-level cues, including style, sentiment, factual truthfulness, and length, explain part but not all of the strongest probe discrimination. These findings together suggest that cross-lingual transfer and shared probe geometry can coexist with model-dependent detection quality and persistent language structure, indicating that representational alignment alone is insufficient evidence of reliable multilingual deception monitoring.

Large Language Models (LLMs) are increasingly used in applications that shape public discourse, yet little is known aboutwhether they reflect distinct opinions on global issues like climate change. This study compares climate change-relatedresponses from multiple LLMs with human opinions collected through the People’s Climate Vote 2024 survey (UNDP – UnitedNations Development Programme and Oxford, 2024). We compare country and LLM”s answer probability distributions and apply Exploratory Factor Analysis (EFA) to identify latent opinion dimensions. Our findings reveal that while LLM responsesdo not exhibit significant biases toward specific demographic groups, they encompass a wide range of opinions, sometimesdiverging markedly from the majority human perspective.