arXiv:2606.11016v1 Announce Type: new Abstract: We ask whether large language models (LLMs) merely imitate rationales when choosing between two options, or whether their choices reflect a systematic underlying decision structure. Using synthetic binary decision settings in which models choose between profiles defined by graded attributes, we compare the attribute a model says mattered most with the attribute that best explains its choice under a behavioural model fit to prior decisions. The behavioural model predicts held-out choices well, showing that model behaviour is systematically related
This research provides a timely, empirical look into LLM decision-making mechanisms, moving beyond anecdotal observations as model capabilities rapidly advance.
Understanding whether LLMs merely imitate or genuinely reflect underlying decision structures is crucial for their reliable deployment in high-stakes environments and for developing truly autonomous agents.
The focus shifts from simply evaluating LLM output to scrutinizing the fidelity and systematicity of their internal decision processes, paving the way for more robust and trustworthy AI applications.
- · AI ethicists
- · AI researchers focusing on explainability
- · Developers of auditable AI systems
- · Developers relying on black-box LLM deployment
- · Applications requiring deep reasoning without transparency
This research directly impacts the design principles for future large language models, emphasizing systematic decision-making over superficial imitation.
It will drive demand for new testing and validation methodologies that can differentiate between surface-level rationalization and deeper behavioural models within AI.
Increased transparency in LLM decision-making could accelerate the adoption of AI agents in critical sectors, conditional on proven reliability and interpretability.
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Read at arXiv cs.AI