Papers by Timo Sztyler
Generating and Evaluating Plausible Explanations for Knowledge Graph Completion (2024.acl-long)
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| Challenge: | Existing XAI approaches focus on learning algorithmic explanations, but are not plausible for users. |
| Approach: | They propose a path-based explanation method that meets human-centric explainability constraints and enhances plausibility. |
| Outcome: | The proposed method meets human-centric explainability constraints and enhances plausibility. |
On Synthesizing Data for Context Attribution in Question Answering (2025.acl-long)
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Gorjan Radevski, Kiril Gashteovski, Shahbaz Syed, Christopher Malon, Sebastien Nicolas, Chia-Chien Hung, Timo Sztyler, Verena Heußer, Wiem Ben Rim, Masafumi Enomoto, Kunihiro Takeoka, Masafumi Oyamada, Goran Glavaš, Carolin Lawrence
| Challenge: | Large Language Models (LLMs) have a tendency to hallucinate, resulting in false or misleading answers. |
| Approach: | They propose a novel generative strategy for synthesizing context attribution data. |
| Outcome: | The proposed approach is highly effective for fine-tuning small LMs for context attribution in different QA tasks and domains. |
A Human-Centric Evaluation Platform for Explainable Knowledge Graph Completion (2024.eacl-demo)
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| Challenge: | Evaluating plausibility, the helpfulness of explanations, is essential for developing eXplainable AI (XAI) that can really aid human users. |
| Approach: | They propose a human-centric evaluation platform to measure plausibility of explanations in the context of eXplainable Knowledge Graph Completion (XKGC) they showcase two use cases to illustrate what results can be achieved with the system. |
| Outcome: | The proposed evaluation platform is designed to evaluate plausibility of explanations in eXplainable Knowledge Graph Completion (XKGC) the proposed evaluation system is based on two use cases in an experimental setting to demonstrate the results. |