aiqbench analysis
This research investigates how linguistic construction choices, beyond simple word selection, systematically alter the political stance judgments of large language models. By applying activation patching techniques to four open-weight models, the study identifies mid-to-late decoder layers as critical regions where these semantic shifts are causally localized and can be restored. The findings highlight significant instability in model reasoning when faced with syntactic variations that preserve meaning but alter structure.
Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing
arXiv:2607.20115v1 Announce Type: new Abstract: 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-weight models show that stance instability affect both meaning-preserving and meaning-inversing rewrites. Because output shifts reveal that rewrites affect stance, but not where in the model, we apply activation patching, where activations from the original statement are substituted into the forward pass for the rewritten statement and measure which components recover the original stance distribution. The results show that mid-to-late decoder layers, especially block outputs at the final prompt position, provide the strongest restoration signal.
Abstract: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-weight models show that stance instability affect both meaning-preserving and meaning-inversing rewrites. Because output shifts reveal that rewrites affect stance, but not where in the model, we apply activation patching, where activations from the original statement are substituted into the forward pass for the rewritten statement and measure which components recover the original stance distribution. The results show that mid-to-late decoder layers, especially block outputs at the final prompt position, provide the strongest restoration signal.
Computation and Language (cs.CL)
arXiv:2607.20115 [cs.CL]
(or arXiv:2607.20115v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.20115 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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