Papers by Jürgen Pfeffer
Measuring What Matters: Evaluating Ensemble LLMs with Label Refinement in Inductive Coding (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are prone to inconsistencies and individual biases, limiting their reliability. |
| Approach: | They propose a framework that combines ensemble methods with code refinement methodology to address these challenges. |
| Outcome: | The proposed framework outperforms large language models and LLMs with a low-rank averaging and a moderator-based mechanism to simulate human consensus. |
SPIN: Sparsifying and Integrating Internal Neurons in Large Language Models for Text Classification (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have revolutionized text classification, but current paradigms rely on output of final layer . implicit internal structures that contribute to LLMs' impressive performance are neglected, forgoing potential performance gains. |
| Approach: | They propose a model-agnostic framework that sparsifies internal neurons of intermediate layers of LLMs for text classification. |
| Outcome: | The proposed framework significantly improves text classification accuracy, efficiency and interpretability. |
Text Annotation via Inductive Coding: Comparing Human Experts to LLMs in Qualitative Data Analysis (2025.findings-naacl)
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| Challenge: | Qualitative data analysis (QDA) is an important research method across fields such as marketing, media studies, social science, psychology, medical research, and others. |
| Approach: | They evaluate the performance of open-source LLMs by comparing them to human experts. |
| Outcome: | The proposed method is based on inductive coding using large language models. |
Digital Gatekeepers: Google’s Role in Curating Hashtags and Subreddits (2025.acl-long)
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| Challenge: | This study examines how search engines like Google selectively promote or suppress certain hashtags and subreddits, impacting the flow of information and impacting public conversations. |
| Approach: | They compare search engine results with nonsampled data from Reddit and Twitter/X to examine how search engines curate content through algorithmic curation. |
| Outcome: | The proposed algorithm suppresses subreddits related to sexually explicit material, conspiracy theories, advertisements, and cryptocurrencies while promoting content associated with higher engagement. |
The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI (2024.findings-emnlp)
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| Challenge: | Psychological trauma can manifest following various distressing events, but studies focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. |
| Approach: | They propose a language model that fine-tunes a single aspect of trauma to better predict traumatic events across domains. |
| Outcome: | The proposed model outperforms large language models on trauma-related datasets . it also outperformed models on court data, counseling conversations, and forum posts . |