Papers by Franziska Weeber
One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization (2026.acl-long)
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| Challenge: | Prior work has used personas to study biases by relying on a single cue to prompt a persona, such as user names or explicit attribute mentions. |
| Approach: | They compare six commonly used personacues across seven open and proprietary LLMs on four writing and advice tasks. |
| Outcome: | The proposed model is based on a persona, a synthetic user profile defined by specific attributes, defined by gender or race. |
Beyond Marginal Distributions: A Framework to Evaluate the Representativeness of Demographic-Aligned LLMs (2026.findings-acl)
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| Challenge: | Existing work on marginal distributions and model steering fails to account for deeper latent structures that characterise real populations. |
| Approach: | They propose a framework for evaluating the representativeness of aligned models through multivariate correlation patterns in addition to marginal distributions. |
| Outcome: | The proposed framework compares two model steering techniques against human responses from the World Values Survey. |
Do Political Opinions Transfer Between Western Languages? An Analysis of Unaligned and Aligned Multilingual LLMs (2026.eacl-long)
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| Challenge: | Public opinion surveys show cross-cultural differences in political opinions between socio-cultural contexts. |
| Approach: | They analyze whether opinions transfer between languages or whether there are separate opinions for each language in multilingual large language models of various sizes across five Western languages. |
| Outcome: | The political alignment shifts opinions almost uniformly across all five languages. |