Right Frame, Wrong Rule: Cultural Cues Expose the Financial Knowledge Gap They Were Meant to Close

• URL: http://arxiv.org/abs/2609.00999v1
• Date: Tue, 01 Sep 2026 09:46:53 GMT
• Title: Right Frame, Wrong Rule: Cultural Cues Expose the Financial Knowledge Gap They Were Meant to Close
• Abstract summary: We study normative pluralism in Islamic finance using a four-choice taxonomy.<n>A cultural cue steers a model toward one framework, but the model selects an incorrect answer within that framework.
• Score: 57.963463622270275
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When a question has valid answers under different normative frameworks, a language model must decide which framework to use and whether it can answer correctly within it. We call this setting normative pluralism and study it in Islamic finance using a four-choice taxonomy that separates framework selection from within-framework correctness. This separation reveals the stereotype trap: a cultural cue steers a model toward one framework, but the model selects an incorrect answer within that framework. Across twelve models, two languages, and fifty demographic signals, cultural cues change framework selection and reveal substantial differences in accuracy, especially among non-frontier models. Under the strongest signal, large open-weight models select the Islamic framework 97% of the time. A two-choice evaluation would report near-perfect alignment, although 57--66% of those selections are incorrect. These findings motivate, but do not directly test, the competence-conditioned routing hypothesis: models may favor frameworks where they are more accurate, while cultural cues may expose framework-specific competence gaps.

Related papers

• We're Cooked! - Probing LLM Political Alignment Via Conflict-Framed Recipe Translation [4.372645928060847]
  Large language models (LLMs) are increasingly deployed for translation tasks, yet their implicit political positioning in such contexts remains understudied.<n>We ask whether a single politically charged framing term, such as aggressor, enemy, neighbour, or coloniser, is sufficient to trigger implicit political alignment in an otherwise apolitical task.<n>Our findings urge caution when deploying LLMs for translation in conflict-adjacent contexts, where implicit political judgments may be made without any signal to the user.
  arXiv  Detail & Related papers  (2026-09-07T14:46:42Z)
• ELBench: A Multi-Dimensional Benchmark for Education-Facing Large Language Models [71.84650682660373]
  Large language models are increasingly deployed in education as tutors, teaching assistants, and content generators.<n>Existing benchmarks evaluate these requirements largely in isolation, so none assesses education-facing suitability as an integrated profile.<n>We introduce ELBench, the first benchmark to evaluate all four requirements (General Capability, Safety and Trustworthiness, Basic Education, and High-Level Cultivation) on the same models under a common protocol.
  arXiv  Detail & Related papers  (2026-08-10T12:46:58Z)
• EduArt: An educational-level benchmark for evaluating art history knowledge in large language models [0.764671395172401]
  Existing art-focused evaluations rely on synthetic questions and rarely report item-level properties.<n>This paper introduces EduArt, an educational-level benchmark for art-historical knowledge and visual reasoning in multimodal LLMs.
  arXiv  Detail & Related papers  (2026-07-02T10:43:06Z)
• Whose Norms? Disentangling Cultural and Personal Alignment in Large Language Models [66.34115263472931]
  Large language models are increasingly used for social decision-making situations that require balancing cultural norms with personal preferences.<n>We introduce PACT, the Personal-Preference and Cultural-Norm Trade-off framework, which evaluates whether models choose to follow a cultural norm or allow personal preferences.<n>Our five-country human study on PACT shows that culture-following in humans is mainly driven by scenario country, with the lowest agreement when participants judge their own cultural contexts.
  arXiv  Detail & Related papers  (2026-06-05T22:20:30Z)
• Training-Free Cultural Alignment of Large Language Models via Persona Disagreement [7.507356092823674]
  We introduce DISCA, an inference-time method that instantiates each country as a panel of World-Values-Survey-grounded persona agents and converts their disagreement into a bounded, loss-averse logit correction.<n>Our results suggest that inference-time calibration is a scalable alternative to fine-tuning for serving the long tail of global moral preferences.
  arXiv  Detail & Related papers  (2026-05-11T16:55:16Z)
• CCD-Bench: Probing Cultural Conflict in Large Language Model Decision-Making [0.9310318514564272]
  Large language models can navigate explicit conflicts between legitimately different cultural value systems.<n>CCD-Bench is a benchmark that assesses decision-making under cross-cultural value conflict.<n>CCD-Bench shifts evaluation beyond isolated bias detection toward pluralistic decision making.
  arXiv  Detail & Related papers  (2025-10-03T22:55:37Z)
• Judging with Confidence: Calibrating Autoraters to Preference Distributions [56.17041629492863]
  We argue that a reliable autorater must learn to model the full distribution of preferences defined by a target population.<n>We present two learning methods tailored to different data conditions.<n>Our results show that finetuning autoraters with a distribution-matching objective leads to verbalized probability predictions that are better aligned with the target preference distribution.
  arXiv  Detail & Related papers  (2025-09-30T20:36:41Z)
• Mathematics Isn't Culture-Free: Probing Cultural Gaps via Entity and Scenario Perturbations [40.515279067334596]
  We create culturally adapted variants of the GSM8K test set for five regions Africa, India, China, Korea, and Japan.<n>We evaluate six large language models (LLMs), ranging from 8B to 72B parameters, across five prompting strategies to assess their robustness to cultural variation in math problem presentation.
  arXiv  Detail & Related papers  (2025-07-01T15:51:46Z)
• Calibrating the Confidence of Large Language Models by Eliciting Fidelity [52.47397325111864]
  Large language models optimized with techniques like RLHF have achieved good alignment in being helpful and harmless. Post-alignment, these language models often exhibit overconfidence, where the expressed confidence does not accurately calibrate with their correctness rate. We propose a plug-and-play method to estimate the confidence of language models.
  arXiv  Detail & Related papers  (2024-04-03T11:36:12Z)
• Why do you think that? Exploring Faithful Sentence-Level Rationales Without Supervision [60.62434362997016]
  We propose a differentiable training-framework to create models which output faithful rationales on a sentence level. Our model solves the task based on each rationale individually and learns to assign high scores to those which solved the task best.
  arXiv  Detail & Related papers  (2020-10-07T12:54:28Z)

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.