1 citations · 2 across the 10 of their papers we have counts for
5 papers · 1 filter
TriQua: Reconciling Granularity and Context in Factuality Evaluation
Jin Liu, Steffen Thoma, Achim Rettinger
The "decompose-then-verify" paradigm for LLM factuality evaluation faces a fundamental trade-off: atomic facts, i.e., one sentence conveying one unit of information, often omit ess…
The Unsampled Truth: Quantifying Prompt Artifacts in LM Psychometrics
Nils Schwager, Christoph Hau, Simon Münker +1
When prompting language models for psychometric assessment, researchers assume that the responses reflect the injected persona and the meaning of the survey item. We test this prem…
LLM Agents Predict Social Media Reactions but Do Not Outperform Text Classifiers: Benchmarking Simulation Accuracy Using 120K+ Personas of 1511 Humans
Ljubisa Bojic, Alexander Felfernig, Bojana Dinic +4
Social media platforms mediate how billions form opinions and engage with public discourse. As autonomous AI agents increasingly participate in these spaces, understanding their be…
Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction
Nils Schwager, Simon Münker, Alistair Plum +1
The transition of Large Language Models (LLMs) from exploratory tools to active "silicon subjects" in social science lacks extensive validation of operational validity. This study…
Next Reply Prediction X Dataset: Linguistic Discrepancies in Naively Generated Content
Simon Münker, Nils Schwager, Kai Kugler +2
The increasing use of Large Language Models (LLMs) as proxies for human participants in social science research presents a promising, yet methodologically risky, paradigm shift. Wh…