Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing
Large language models (LLMs) are known to be sensitive to prompt and input formulations. However, existing studies have focused on lexical realization and largely ignored constructional choice. This paper studies whether linguistic construction can systematically shift LLM decisions and where these shifts can be causally localized inside the model. We use political stance judgment as a meaning-sensitive case study and extend an English political statements dataset, resulting in six controlled linguistic rewrite types that preserve or invert the meaning of a statement. Experiments on four open-
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- PossiblePossibly related (embedding) · 48%Are LLMs Stifling Political Speech? An Assessment of How AI Models Protect Free Expression - The Oversight Board →
- LinkedLinked via arxiv author · 85%Langchen Huang →
“Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing”
- LinkedLinked via arxiv author · 85%Sebastian Padó →
“Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing”
- LinkedLinked via arxiv author · 85%Franziska Weeber →
“Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing”
