Papers by Wolfgang Nejdl
When Facts Change: Temporal Knowledge Conflict Resolution in LLMs (2026.findings-acl)
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| Challenge: | Large language models are increasingly used in retrieval-augmented generation systems to reconcile knowledge conflicts between parametric memory and contextual inputs. |
| Approach: | They propose to use mutability to resolve temporal misalignment in large language models to compare stable and recently updated facts from Wikidata to determine if mutable models can serve as a mediating signal in this process. |
| Outcome: | The proposed model can produce reasoning for facts that actually changed but rarely for stable ones, whereas smaller models rarely detect conflict, while larger models detect it but fail to act on mutability judgments. |
TIGQA: An Expert-Annotated Question-Answering Dataset in Tigrinya (2024.lrec-main)
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| Challenge: | Existing annotated datasets for NLP tasks in languages with limited resources are limited. |
| Approach: | They propose to use machine translation to convert existing Tigrinya dataset into a Tigrina dataset in SQuAD format. |
| Outcome: | The proposed dataset is an expert-annotated Tigrinya dataset with 2,685 question-answer pairs covering 122 diverse topics. |
Data Drift in Clinical Outcome Prediction from Admission Notes (2024.lrec-main)
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Paul Grundmann, Jens-Michalis Papaioannou, Tom Oberhauser, Thomas Steffek, Amy Siu, Wolfgang Nejdl, Alexander Loeser
| Challenge: | a pivotal dataset for clinical NLP research was released in 2016 . public access to such datasets is limited due to privacy and ethical concerns . |
| Approach: | They propose a novel clinical outcome prediction dataset based on MIMIC-IV . they provide initial insights into the performance of models trained on MIDIC-III . |
| Outcome: | The proposed dataset aims to probe the robustness and generalization of clinical outcome prediction models . the study focuses on challenges tied to evolving documentation standards and changing codes in the ICD taxonomy . |
This Patient Looks Like That Patient: Prototypical Networks for Interpretable Diagnosis Prediction from Clinical Text (2022.aacl-main)
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Betty van Aken, Jens-Michalis Papaioannou, Marcel Naik, Georgios Eleftheriadis, Wolfgang Nejdl, Felix Gers, Alexander Loeser
| Challenge: | a novel method for diagnosis prediction from clinical text is needed in clinical practice . prototypical part networks and label-wise attention are used to make models interpretable and helpful . |
| Approach: | They propose a deep neural model that makes predictions based on parts of the text that are similar to prototypical patients. |
| Outcome: | The proposed method outperforms baseline models on two clinical datasets and provides valuable explanations for clinical decision support. |
CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models (2026.eacl-long)
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| Challenge: | generative large language models are being investigated for complex medical tasks, but their effectiveness in real-world clinical applications remains underexplored. |
| Approach: | They propose to compare encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in a MIMIC-IV dataset. |
| Outcome: | The proposed benchmark compares encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in the MIMIC-IV dataset. |
MoVoC: Morphology-Aware Subword Construction for Ge’ez Script Languages (2025.findings-emnlp)
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| Challenge: | Subword-based tokenization methods fail to preserve morphological boundaries, a limitation especially pronounced in low-resource, morphology complex languages such as those written in the Ge‘ez script. |
| Approach: | They propose a tokenizer that integrates supervised morphological analysis into the subword vocabulary and propose morpheme-based tokenization with Byte Pair Encoding (BPE) tokens. |
| Outcome: | The proposed tokenizer preserves morphological integrity while maintaining lexical meaning. |
Toxicity, Morality, and Speech Act Guided Stance Detection (2023.findings-emnlp)
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| Challenge: | Existing studies that focus on stance detection ignore the speech act, toxic, and moral features of tweets or lack an efficient architecture to detect the attitudes across targets. |
| Approach: | They propose a multitasking model that extracts valence, arousal, and dominance aspects hidden in tweets and injects the emotional sense into the embedded text followed by an efficient attention framework to correctly detect the tweet’s stance. |
| Outcome: | The proposed model exploits the toxicity, morality, and speech act features of the tweets to detect the public's stance. |