arXiv AI August 7, 2026 By Yifan Lyu, Xinran Li, Jiaqi Qiao, Xiujuan Xu

Signal or Spurious Cue? A Randomized Audit of Survey-Country Metadata in LLM Social Inference

arXiv:2608. 06085v1 Announce Type: new Abstract: Survey-country metadata can improve an LLM's forecast of an individual response when informative, yet the same cue may redirect the forecast when assigned at random.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Sep 1

Measurement Validity in LLM Cultural Alignment

arXiv:2608.29266v1 Announce Type: cross Abstract: Researchers increasingly treat LLM survey responses as a proxy for human cultural values. This includes projecting model outputs onto instruments lik...

By An Duy Nguyen, Muhammad Aurangzeb Ahmad

llms safety

Hugging Face Trending Papers

Sep 22

The Limits of Simulated Societies: How Post-Training and Survey Fine-Tuning Erase Cross-Cultural Variance

Using large language models (LLMs) to simulate diverse human populations has the potential to transform many aspects of computational social science, yet many evaluations score the average response ra...

llms reinforcement-learning fine-tuning safety

arXiv AI

Sep 15

Before You Poll with LLMs: A Deliberative Diagnostic Framework

arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM per...

By Ahmed Wali, Hassaan Tayyab

llms benchmarks

arXiv Machine Learning

Jul 24

Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets

arXiv:2607. 20441v1 Announce Type: cross Abstract: Every information ecosystem produces beliefs that shape strategic decisions.

By Mykola Khandoga, Yevhen Kostiuk, Anton Polishko, Yurii Filipchuk, Kostiantyn Kozlov, Dmytro Zamriy, Artur Kiulian

llms benchmarks safety

Hugging Face Trending Papers

Jul 29

OptimismBench: Forecasting Bias and the Alignment Effect in Language Model Judgment

Large language models are increasingly used as decision aids whose probability judgments shape downstream choices. Whether those judgments carry a systematic directional tilt has been hard to detect: calibration metrics aggregate unsigned errors, and naturalistic uncertainty offers no ground-truth probability.

llms safety

arXiv AI

Aug 21

Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay

arXiv:2608. 19760v1 Announce Type: cross Abstract: Audited against causal ground truth from executed replay in a single-agent tool environment (ALFWorld), none of the step-level credit signals used to train LLM agents -- LLM-judge scores, outcome-conditioned logprob ratios, or the policy's own confidence -- identifies which steps causally matter better than chance.

By Haiyue Zhang

llms agents

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Related stories

Measurement Validity in LLM Cultural Alignment

The Limits of Simulated Societies: How Post-Training and Survey Fine-Tuning Erase Cross-Cultural Variance

Before You Poll with LLMs: A Deliberative Diagnostic Framework

Belief Propagation in LLM World Models: Measuring Strategic Information Bias with Prediction Markets

OptimismBench: Forecasting Bias and the Alignment Effect in Language Model Judgment

Credit Without Ground Truth: Auditing Step-Level Credit Assignment in LLM Agents Against Executed Replay