Papers by Vera Neplenbroek
LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks (2025.acl-short)
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Anna Bavaresco, Raffaella Bernardi, Leonardo Bertolazzi, Desmond Elliott, Raquel Fernández, Albert Gatt, Esam Ghaleb, Mario Giulianelli, Michael Hanna, Alexander Koller, Andre Martins, Philipp Mondorf, Vera Neplenbroek, Sandro Pezzelle, Barbara Plank, David Schlangen, Alessandro Suglia, Aditya K Surikuchi, Ece Takmaz, Alberto Testoni
| Challenge: | Existing evaluations of NLP models with LLMs are based on human judgments . however, there are concerns about their validity and reproducibility in proprietary models . |
| Approach: | They evaluate 11 current LLMs for their ability to replicate annotations. they show substantial variance across models and datasets. |
| Outcome: | The proposed model can replicate human annotations on 20 NLP datasets and show substantial variance across models and datasets. |
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. |
Reading Between the Prompts: How Stereotypes Shape LLM’s Implicit Personalization (2025.emnlp-main)
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| Challenge: | Prior work has shown that such inferences can lead to lower quality responses for users assumed to be from minority groups. |
| Approach: | They analyze LLMs' latent user representations through both model internals and generated answers to targeted user questions. |
| Outcome: | The proposed models infer demographic attributes based on stereotypical signals, which persists even when the user explicitly identifies with a different demographic group. |
Actionable Interpretability for Churn Classification: A Text Bottleneck Model Case Study at a Major Telecom Provider (2026.acl-industry)
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| Challenge: | Managing customer churn is vital for subscription-based businesses . large language models (LLMs) can automate the classification of chursn-intent at scale . lack of transparency forces a difficult choice between automated systems and manual review . |
| Approach: | They propose to use text bottleneck models to classify customer churn in subscription-based businesses . they show that the model can be used to bridge the perceived trade-off between interpretability andpredictive performance . |
| Outcome: | The proposed model performs competitively with black-box baselines and an interactive dashboard. |
Cross-Lingual Transfer of Debiasing and Detoxification in Multilingual LLMs: An Extensive Investigation (2025.findings-acl)
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| Challenge: | Prior work has shown that finetuning on specialized datasets can mitigate this behavior, and doing so in English can transfer to other languages. |
| Approach: | They propose to fine tune generative large language models to provide safe responses to harmful user input and to use direct preference optimization to mitigate toxicity. |
| Outcome: | The proposed models show that finetuning on specialized datasets reduces biases but also produces fluent and diverse text in non-English languages. |