When Helpfulness Overrides Causal Caution: Context-Dependent Suppression and Recovery in LLMs
arXiv:2606.24370v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly integrated into decision-support roles in business and policy contexts. While prior benchmark studies have primarily evaluated LLMs' causal reasoning capabilities, a more fundamental epistemic dimension has been overlooked: Causal Caution, defined as the propensity to refrain from causal judgment when empirical evidence is insufficient. This study examines the systematic suppression of Causal Caution that occurs when LLMs shift from academic to practical advisory contexts. Using an evaluation rubric i
This study is being published now as LLMs are rapidly moving from academic benchmarks to practical, decision-support roles, necessitating a deeper understanding of their real-world behaviors.
A strategic reader should care because this research highlights a critical vulnerability in LLMs' decision-making when applied to contexts requiring cautious judgment, especially in business and policy.
Our understanding of LLM reliability changes, placing greater emphasis on evaluating 'Causal Caution' alongside traditional causal reasoning, particularly in high-stakes applications.
- · Companies specializing in LLM verification and validation
- · Developers of 'cautious AI' frameworks
- · Risk management and compliance sectors
- · Organizations deploying LLMs uncritically in advisory roles
- · LLM developers who prioritize 'helpfulness' over epistemological rigor
- · Users relying on LLMs for critical causal judgments without oversight
Increased scrutiny and demand for 'explainable and cautious AI' capabilities in LLMs across industries.
Development of new benchmark standards and regulatory frameworks specifically addressing LLM caution and ethical decision-making.
A potential slowdown in the uncritical adoption of LLMs in highly sensitive areas until these 'Causal Caution' issues are demonstrably addressed.
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Read at arXiv cs.AI