Jan Trienes
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View article: Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support
Marcel: A Lightweight and Open-Source Conversational Agent for University Student Support Open
We present Marcel, a lightweight and open-source conversational agent designed to support prospective students with admission-related inquiries. The system aims to provide fast and personalized responses, while reducing workload of univers…
View article: Behavioral Analysis of Information Salience in Large Language Models
Behavioral Analysis of Information Salience in Large Language Models Open
Large Language Models (LLMs) excel at text summarization, a task that requires models to select content based on its importance. However, the exact notion of salience that LLMs have internalized remains unclear. To bridge this gap, we intr…
View article: Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding
Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding Open
Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can strug…
View article: FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence Open
Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medicine? This paper presents FactPICO, a factuality benchmark …
View article: InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification Open
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework to characterize and recover simplification-induced informa…
View article: Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"
Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis" Open
This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study. Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. Guidance in Radiology Repor…
View article: Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis"
Model outputs for the study "Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis" Open
This resources provides pre-processed input data, model checkpoints and model outputs for experiments on the OpenI dataset in below study. Jan Trienes, Paul Youssef, Jörg Schlötterer, and Christin Seifert. 2023. Guidance in Radiology Repor…
View article: Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis Open
Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to enhance content selection and factuality through guided abstr…
View article: From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Open
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are…
View article: Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis Open
Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to enhance content selection and factuality through guided abstr…
View article: From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Open
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are…
View article: Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset
Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset Open
Automatic text simplification can help patients to better understand their own clinical notes. A major hurdle for the development of clinical text simplification methods is the lack of high quality resources. We report ongoing efforts in c…
View article: Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records Open
A major hurdle in the development of natural language processing (NLP) methods for Electronic Health Records (EHRs) is the lack of large, annotated datasets. Privacy concerns prevent the distribution of EHRs, and the annotation of data is …
View article: Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records Open
Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove per…
View article: Comparing rule-based, feature-based and deep neural methods for de-identification of Dutch medical records
Comparing rule-based, feature-based and deep neural methods for de-identification of Dutch medical records Open
Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove per…
View article: Recommending Users: Whom to Follow on Federated Social Networks
Recommending Users: Whom to Follow on Federated Social Networks Open
To foster an active and engaged community, social networks employ recommendation algorithms that filter large amounts of contents and provide a user with personalized views of the network. Popular social networks such as Facebook and Twitt…