Papers by Jan Trienes
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification (2024.acl-long)
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Jan Trienes, Sebastian Joseph, Jörg Schlötterer, Christin Seifert, Kyle Lo, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. |
| Approach: | They propose a framework to characterize and recover simplification-induced information loss in form of question-and-answer (QA) pairs. |
| Outcome: | The proposed framework characterizes and recovers simplification-induced information loss in form of question-and-answer (QA) pairs. |
Behavioral Analysis of Information Salience in Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models excel at text summarization, but the exact notion of salience remains unclear. |
| Approach: | They propose a framework to derive and investigate information salience in Large Language Models (LLMs) using length-controlled summarization as a behavioral probe into the content selection process. |
| Outcome: | The proposed framework derives a proxy for how models prioritize information in large language models. |
Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support (2025.emnlp-demos)
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Jan Trienes, Anastasiia Derzhanskaia, Roland Schwarzkopf, Markus Mühling, Jörg Schlötterer, Christin Seifert
| Challenge: | Existing systems that provide contextually relevant information are difficult to deploy in a university setting . a number of universities are developing or using chatbots to support prospective students . |
| Approach: | They propose a conversational agent called Marcel that uses retrieval-augmented generation to provide contextually relevant information. |
| Outcome: | The proposed system is designed to provide fast and personalized responses while reducing workload. |
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence (2024.acl-long)
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Sebastian Joseph, Lily Chen, Jan Trienes, Hannah Göke, Monika Coers, Wei Xu, Byron Wallace, Junyi Jessy Li
| Challenge: | FactPICO is a factuality benchmark for plain language summarization of medical texts describing randomized controlled trials . existing metrics for factual summarizing medical evidence are poorly correlated with expert judgments on the instance level. |
| Approach: | They propose a factuality benchmark for plain language summarization of medical texts . they assess factuality of critical elements of RCTs in those summaries . |
| Outcome: | The proposed benchmark assesses the factuality of medical summaries using LLMs . the summary summators are based on 345 plain language summaires with fine-grained evaluation . |
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding (2024.lrec-main)
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Ahmad Idrissi-Yaghir, Amin Dada, Henning Schäfer, Kamyar Arzideh, Giulia Baldini, Jan Trienes, Max Hasin, Jeanette Bewersdorff, Cynthia S. Schmidt, Marie Bauer, Kaleb E. Smith, Jiang Bian, Yonghui Wu, Jörg Schlötterer, Torsten Zesch, Peter A. Horn, Christin Seifert, Felix Nensa, Jens Kleesiek, Christoph M. Friedrich
| Challenge: | Pre-trained language models can struggle in specialized domains such as medicine . existing generalpurpose pre-tried models can be used and refined through further pre-training on domainspecific unlabeled data. |
| Approach: | They pre-trained German medical language models on 2.4B tokens from translated public data and 3B token of German clinical data. |
| Outcome: | The proposed models outperform clinical models on various downstream tasks in germany . the authors show that continuous pre-training can match or exceed clinical models trained from scratch . |