Papers by Marlene Lutz
Persona-driven Simulation of Voting Behavior in the European Parliament with Large Language Models (2026.findings-eacl)
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| Challenge: | Large Language Models exhibit a progressive left-leaning bias, but can also produce behavior that aligns with socioeconomic groups. |
| Approach: | They analyze whether persona prompting can accurately predict individual voting decisions . they find that they can simulate the voting behavior of European Parliament members reasonably well . |
| Outcome: | The proposed model can predict the voting behavior of European Parliament members reasonably well, with a weighted F1 score of approximately 0.793. |
The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Models (2025.findings-emnlp)
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| Challenge: | persona prompting is increasingly used in large language models to simulate views of various sociodemographic groups. |
| Approach: | They use open-source LLMs to study how persona prompts influence LLM simulations . they use role adoption formats and demographic priming strategies to study marginalized groups . |
| Outcome: | The results show that the choice of demographic priming and role adoption strategy significantly impacts their portrayal. |
Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs (2025.findings-acl)
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| Challenge: | 211 studies on the demographic representativeness of large language models have conflicting results . 29% of the studies report positive conclusions on the representativeness, 30% do not evaluate LLMs across multiple demographic categories or within demographic subcategories. |
| Approach: | 211 papers review the representativeness of large language models . authors recommend more precise evaluation methods and comprehensive documentation of demographic attributes . |
| Outcome: | 211 studies on the representativeness of large language models are reviewed . 29% of the studies report positive conclusions, but 30% fail to specify subcategories . authors recommend more precise evaluation methods and documentation of demographic attributes . |
Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality (2026.eacl-long)
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| Challenge: | Psychometric tests are increasingly used to assess psychological constructs in large language models (LLMs). |
| Approach: | They evaluate the reliability and validity of human psychometric tests on 17 LLMs for three constructs: sexism, racism, and morality. |
| Outcome: | The results show that the psychometric tests on 17 LLMs do not align, and in some cases negatively correlate with, model behavior in downstream tasks, indicating low ecological validity. |
Local Contrastive Editing of Gender Stereotypes (2024.emnlp-main)
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| Challenge: | Stereotypical bias encoded in language models (LMs) poses a threat to safe language technology . current research lacks a thorough understanding of manifestations of biases in specific model weights. |
| Approach: | They propose a method that localizes and edits weights associated with gender bias . they use local contrastive editing to localize and control a small subset of weights . |
| Outcome: | The proposed method localizes and controls a small subset of weights that encode gender bias. |
SensePOLAR: Word sense aware interpretability for pre-trained contextual word embeddings (2022.findings-emnlp)
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| Challenge: | Existing word embedding models lack interpretability for words . |
| Approach: | They propose to add interpretability to word embeddings by using a POLAR framework that enables wordsense aware interpretations for pre-trained contextual word embeds. |
| Outcome: | The proposed framework achieves comparable performance to existing embeddings across GLUE and SQuAD benchmarks. |