aiqbench analysis
This study investigates the internal decision-making structure of large language models by contrasting their stated rationales with behaviorally inferred priorities in synthetic binary choice tasks. The findings reveal a phenomenon termed "superficial belief," where models exhibit structured, predictable choices driven by probabilistic attribute weighting but possess only limited verbal access to these underlying drivers. This distinction is critical for understanding the gap between an LLM's explicit explanations and its actual reasoning mechanisms, offering insights into model interpretability and reliability.
Superficial Beliefs in LLM Decision-Making
arXiv:2606.11016v2 Announce Type: replace 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 to the visible attributes rather than being random. However, direct self-reports and a separate score-based judge recover the behaviourally inferred driver only partially. The resulting picture is neither one of arbitrary behaviour nor one of fully articulated belief - outputs are structured enough to support prediction, but explicit reasons track the recovered driver only imperfectly. This qualitative pattern persists across prompt-order and sampling perturbations, alternative behavioural models, targeted occlusion analyses, and structurally varied decision settings. We interpret this as evidence for ``superficial belief'' in LLM decision-making: models behave as if guided by probabilistic local priorities over attributes, while having only limited verbal access to the attributes that drive their decisions.
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 to the visible attributes rather than being random. However, direct self-reports and a separate score-based judge recover the behaviourally inferred driver only partially. The resulting picture is neither one of arbitrary behaviour nor one of fully articulated belief - outputs are structured enough to support prediction, but explicit reasons track the recovered driver only imperfectly. This qualitative pattern persists across prompt-order and sampling perturbations, alternative behavioural models, targeted occlusion analyses, and structurally varied decision settings. We interpret this as evidence for ``superficial belief'' in LLM decision-making: models behave as if guided by probabilistic local priorities over attributes, while having only limited verbal access to the attributes that drive their decisions.
Artificial Intelligence (cs.AI)
arXiv:2606.11016 [cs.AI]
(or arXiv:2606.11016v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2606.11016 Focus to learn more arXiv-issued DOI via DataCite
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