Superficial Beliefs in LLM Decision-Making

• URL: http://arxiv.org/abs/2606.11016v1
• Date: Tue, 09 Jun 2026 15:54:35 GMT
• Title: Superficial Beliefs in LLM Decision-Making
• Abstract summary: We ask whether large language models (LLMs) merely imitate rationales when choosing between two options.<n>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.<n>We interpret this as evidence for superficial belief'' in LLM decision-making.
• Score: 17.52079171010035
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Minimal Model of Bounded Trade-Off Screening in Multi-Attribute Choice [12.68567713768202]
  We propose a bounded trade-off reasoning framework in which decisions are governed by a screening process that evaluates the balance between gains and losses across attributes.<n>We show that this mechanism produces preference patterns that differ from standard utility-based models and captures context-dependent variation in trade-off behavior.<n>These results establish bounded trade-off screening as a plausible computational mechanism for multi-attribute choice and generate testable predictions for future behavioral studies.
  arXiv  Detail & Related papers  (2026-06-11T11:12:37Z)
• Can Revealed Preferences Clarify LLM Alignment and Steering? [23.175180848107093]
  We present an empirical pipeline for estimating the implied preferences that an LLM's observed choices optimize.<n>We show how this revealed-preference description allows rigorous evaluation of whether models behave in a consistently goal-directed way.
  arXiv  Detail & Related papers  (2026-05-08T23:26:35Z)
• MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions [50.00784452900918]
  We propose a general, model-based debiasing (MBD) framework that addresses this challenge.<n>We explicitly estimate the contextual mean and variance of the engagement distribution for arbitrary cohorts.<n>This integration allows the framework to convert biased raw signals into unbiased representations.
  arXiv  Detail & Related papers  (2026-03-15T15:07:01Z)
• Model Correlation Detection via Random Selection Probing [62.093777777813756]
  Existing similarity-based methods require access to model parameters or produce scores without thresholds.<n>We introduce Random Selection Probing (RSP), a hypothesis-testing framework that formulates model correlation detection as a statistical test.<n>RSP produces rigorous p-values that quantify evidence of correlation.
  arXiv  Detail & Related papers  (2025-09-29T01:40:26Z)
• Leveraging Predictive Equivalence in Decision Trees [25.79772180003752]
  Decision trees are widely used for interpretable machine learning.<n>We present a representation of decision trees that does not exhibit predictive equivalence.<n>We show that decision trees are surprisingly robust to test-time missingness of feature values.
  arXiv  Detail & Related papers  (2025-06-17T03:11:30Z)
• Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors [61.92704516732144]
  We show that the most robust features for correctness prediction are those that play a distinctive causal role in the model's behavior.<n>We propose two methods that leverage causal mechanisms to predict the correctness of model outputs.
  arXiv  Detail & Related papers  (2025-05-17T00:31:39Z)
• Predicting is not Understanding: Recognizing and Addressing Underspecification in Machine Learning [47.651130958272155]
  Underspecification refers to the existence of multiple models that are indistinguishable in their in-domain accuracy. We formalize the concept of underspecification and propose a method to identify and partially address it.
  arXiv  Detail & Related papers  (2022-07-06T11:20:40Z)
• An exact counterfactual-example-based approach to tree-ensemble models interpretability [0.0]
  High-performance models do not exhibit the necessary transparency to make their decisions fully understandable. We could derive an exact geometrical characterisation of their decision regions under the form of a collection of multidimensional intervals. An adaptation to reasoning on regression problems is also envisaged.
  arXiv  Detail & Related papers  (2021-05-31T09:32:46Z)
• Characterizing Fairness Over the Set of Good Models Under Selective Labels [69.64662540443162]
  We develop a framework for characterizing predictive fairness properties over the set of models that deliver similar overall performance. We provide tractable algorithms to compute the range of attainable group-level predictive disparities. We extend our framework to address the empirically relevant challenge of selectively labelled data.
  arXiv  Detail & Related papers  (2021-01-02T02:11:37Z)
• Selecting Diverse Models for Scientific Insight [0.12891210250935145]
  We show how different penalty settings can promote either shrinkage or sparsity of coefficients in separate models. A choice of penalty form that enforces variable selection is applied to predict stacking fault energy from steel alloy composition.
  arXiv  Detail & Related papers  (2020-06-16T14:06:55Z)
• Decision-Making with Auto-Encoding Variational Bayes [71.44735417472043]
  We show that a posterior approximation distinct from the variational distribution should be used for making decisions. Motivated by these theoretical results, we propose learning several approximate proposals for the best model. In addition to toy examples, we present a full-fledged case study of single-cell RNA sequencing.
  arXiv  Detail & Related papers  (2020-02-17T19:23:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.