Author

Sebastian Pohl

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#artificial intelligence Jun 2026

Who Annotates in NLP? A Large-scale Assessment of Human Annotation Reporting between 2018 and 2025

The results show that annotation reporting in NLP has improved over time but remains uneven, and they establish a scalable framework and bare-minimum reporting recommendations for making human annotation more reliable, reproducible, and interpretable.

M. Kunilovskaya, Gagan Bhatia, Lisa Sophie Albertelli et al. · arXiv.org · 0 citations

Jul 2026

LLMs struggle to simulate human belief updates in controlled environments

LLMs are increasingly deployed as proxies for human study participants in social science experiments, yet the fidelity of this practice has rarely been tested directly. We test whether six LLMs can simulate individual human belief updates, comparing LLM outputs 1-to-1 against ground truth data from 391 UK participants...

Sebastian Pohl, Harsh Mehta, Pranav Mambayil et al. · arXiv.org · 0 citations

#artificial intelligence May 2026

DRIP-R: A Benchmark for Decision-Making and Reasoning Under Real-World Policy Ambiguity in the Retail Domain

DRIP-R is introduced, a benchmark that systematically exploits real-world retail policy ambiguities to construct scenarios in which no single correct resolution exists, and shows that frontier models fundamentally disagree on identical policy-ambiguous scenarios, confirming that ambiguity poses a genuine and systematic...

Hsuvas Borkakoty, Sebastian Pohl, Cheng Wang et al. · arXiv.org · 0 citations