Understanding the Impact of Linguistic Realization Choices on LLM Stance with Causal Tracing

2026-07-23 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, extended

Summary

This study investigates how controlled linguistic rewrites influence Large Language Model (LLM) political stance judgments and where these shifts are causally localized. Researchers extended the ProbVAA dataset with six rewrite types: explicit negations, semantic opposites (meaning-reversing), active/passive conversions, it-clefts, wh-clefts, and support verb constructions (meaning-preserving). Experiments on Gemma-3-4B-IT, Gemma-3-12B-IT, Qwen3-4B, and Qwen3-14B models revealed that stance instability occurs with both meaning-preserving and meaning-inverting edits, with larger models exhibiting lower flip rates but more neutral responses. Variance decomposition confirmed that linguistic articulation, not just stochastic sampling, significantly drives stance shifts, causing up to 2.06 Likert point changes. Causal tracing via activation patching localized these shifts to mid-to-late decoder block outputs at the final prompt position, indicating a shared downstream mechanism for propagating judgment changes.

Key takeaway

For NLP engineers building reliable LLM applications, recognize that linguistic construction choices, even meaning-preserving ones like active/passive voice, can systematically alter model judgments. Your evaluations should extend beyond lexical paraphrases to include structural linguistic variations. If unexpected stance shifts occur, utilize causal tracing methods like activation patching to pinpoint the specific mid-to-late decoder layers responsible, enabling more targeted model debugging and robustness improvements.

Key insights

Linguistic construction choices systematically shift LLM political stance, with these changes localizing to mid-to-late decoder layers.

Principles

• LLM stance is sensitive to linguistic realization, not just propositional content.
• Larger LLMs show lower articulation sensitivity but more neutral judgments.
• Stance shifts are causally mediated by mid-to-late decoder block outputs.

Method

Extend political stance datasets with controlled meaning-preserving and meaning-reversing linguistic rewrites. Apply activation patching to localize causal components of stance shifts.

In practice

• Test LLM robustness to active/passive, clefts, and support verb constructions.
• Use activation patching to diagnose internal components causing output shifts.

Topics

• Large Language Models
• Linguistic Rewrites
• Causal Tracing
• Activation Patching
• Political Stance
• Model Robustness

Code references

• nobody294/linguistic_rule_triggers

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.