dorsal/arxiv
View SchemaSyntactic Framing Fragility: An Audit of Robustness in LLM Ethical Decisions
| Authors | Katherine Elkins, Jon Chun |
|---|---|
| Categories | |
| ArXiv ID | 2601.09724vv1 |
| URL | https://arxiv.org/abs/2601.09724 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Large language models (LLMs) are increasingly deployed in consequential decision-making settings, yet their robustness to benign prompt variation remains underexplored. In this work, we study whether LLMs maintain consistent ethical judgments across logically equivalent but syntactically different prompts, focusing on variations involving negation and conditional structure. We introduce Syntactic Framing Fragility (SFF), a robustness evaluation framework that isolates purely syntactic effects via Logical Polarity Normalization (LPN), enabling direct comparison of decisions across positive and negative framings without semantic drift. Auditing 23 state-of-the-art models spanning the U.S. and China as well as small U.S. open-source software models over 14 ethical scenarios and four controlled framings (39,975 decisions), we find widespread and statistically significant inconsistency: many models reverse ethical endorsements solely due to syntactic polarity, with open-source models exhibiting over twice the fragility of commercial counterparts. We further uncover extreme negation sensitivity, where some models endorse actions in 80-97% of cases when explicitly prompted with "should not." We show that eliciting chain-of-thought reasoning substantially reduces fragility, identifying a practical mitigation lever, and we map fragility across scenarios, finding higher risk in financial and business contexts than in medical scenarios. Our results demonstrate that syntactic consistency constitutes a distinct and critical dimension of ethical robustness, and we argue that SFF-style audits should be a standard component of safety evaluation for deployed LLMs. Code and results will be available on github.com.
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"abstract": "Large language models (LLMs) are increasingly deployed in consequential decision-making settings, yet their robustness to benign prompt variation remains underexplored. In this work, we study whether LLMs maintain consistent ethical judgments across logically equivalent but syntactically different prompts, focusing on variations involving negation and conditional structure. We introduce Syntactic Framing Fragility (SFF), a robustness evaluation framework that isolates purely syntactic effects via Logical Polarity Normalization (LPN), enabling direct comparison of decisions across positive and negative framings without semantic drift. Auditing 23 state-of-the-art models spanning the U.S. and China as well as small U.S. open-source software models over 14 ethical scenarios and four controlled framings (39,975 decisions), we find widespread and statistically significant inconsistency: many models reverse ethical endorsements solely due to syntactic polarity, with open-source models exhibiting over twice the fragility of commercial counterparts. We further uncover extreme negation sensitivity, where some models endorse actions in 80-97% of cases when explicitly prompted with \"should not.\" We show that eliciting chain-of-thought reasoning substantially reduces fragility, identifying a practical mitigation lever, and we map fragility across scenarios, finding higher risk in financial and business contexts than in medical scenarios. Our results demonstrate that syntactic consistency constitutes a distinct and critical dimension of ethical robustness, and we argue that SFF-style audits should be a standard component of safety evaluation for deployed LLMs. Code and results will be available on github.com.",
"arxiv_id": "2601.09724",
"authors": [
"Katherine Elkins",
"Jon Chun"
],
"categories": [
"cs.CL",
"cs.AI"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Syntactic Framing Fragility: An Audit of Robustness in LLM Ethical Decisions",
"url": "https://arxiv.org/abs/2601.09724",
"version": "v1"
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