arXiv: cs.AI preprintsInternational6 November 2026
Evaluating Token Probabilities as Confidence Estimators in Human Behavior Simulations: A Case Study in Luxembourgish Next Like Prediction
This is an official announcement record
Firsthand records what arXiv: cs.AI preprints announced and links to the original. The wording below is theirs, not ours.
arXiv:2611.07735v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly used to simulate human behavior, their operational validity is typically assessed through accuracy alone, implicitly treating human behavior as deterministic. We argue that on noisy digital trace data, a model's performance depends not only on predicting the correct outcome but on its calibration. We present a case study testing whether the token probabilities of open-weight LLMs can serve as confidence estimates in a Next Like Prediction task: predicting how a user of the Luxembourgish news plat
Read the official announcement
Opens arxiv.org
More from arXiv: cs.AI preprints
- Formalizing inverse problems in the age of AI: possibilities, pitfalls, and an invitation6 November 2026
- KDASO: Knowledge-Data Dual-Driven Automated Skill Optimization for Liability Adjudication Tasks6 November 2026
- BlockLock: Reproducible LLM Inference with Fixed Batch Geometry6 November 2026
- Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism6 November 2026
- The Two-Ledger Model: Transaction-Grain Cost Accounting for Multi-Agent AI Systems Across Clouds, Models, and Systems of Record6 November 2026
This content is for informational purposes only and is not professional advice. Specifications, prices, plan tiers, and features change frequently and may differ from what is shown here; verify current details on the manufacturer's or company's official page before purchasing. Ratings are based on analysis of published documentation, not independent lab testing.