18 citations · 31 across the 25 of their papers we have counts for
9 papers · 1 filter
Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study
Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch +5
In recent research, large language models (LLMs) have been increasingly used to investigate public opinions. This study investigates the algorithmic fidelity of LLMs, i.e., the abi…
AI Conversational Interviewing: Transforming Surveys with LLMs as Adaptive Interviewers
Alexander Wuttke, Matthias Aßenmacher, Christopher Klamm +3
Traditional methods for eliciting people's opinions face a trade-off between depth and scale: structured surveys enable large-scale data collection but limit respondents' ability t…
Problem Solving Through Human-AI Preference-Based Cooperation
Subhabrata Dutta, Timo Kaufmann, Goran Glavaš +7
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved.…
The Potential and Challenges of Evaluating Attitudes, Opinions, and Values in Large Language Models
Bolei Ma, Xinpeng Wang, Tiancheng Hu +4
Recent advances in Large Language Models (LLMs) have sparked wide interest in validating and comprehending the human-like cognitive-behavioral traits LLMs may capture and convey. T…
Understanding Jailbreak Success: A Study of Latent Space Dynamics in Large Language Models
Sarah Ball, Frauke Kreuter, Nina Panickssery
Conversational large language models are trained to refuse to answer harmful questions. However, emergent jailbreaking techniques can still elicit unsafe outputs, presenting an ong…
Position: Insights from Survey Methodology can Improve Training Data
Stephanie Eckman, Barbara Plank, Frauke Kreuter
Whether future AI models are fair, trustworthy, and aligned with the public's interests rests in part on our ability to collect accurate data about what we want the models to do. H…