Papers by Jonas Wallat
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. |
A Study into Investigating Temporal Robustness of LLMs (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are limited in their ability to process temporal information and perform tasks requiring temporal reasoning and factual knowledge. |
| Approach: | They propose to use eight time-sensitiverobustness tests to test the model's temporal robustness for user questions in the zero-shot setting. |
| Outcome: | The proposed tests improve the temporal QA performance by up to 55%. |