Papers by Lukas Stappen
CarMem: Enhancing Long-Term Memory in LLM Voice Assistants through Category-Bounding (2025.coling-industry)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are stateless and present all relevant memories during each interaction, resulting in repetitive user requests and disengagement. |
| Approach: | They propose a long-term memory system for voice assistants structured around predefined categories that leverages Large Language Models to extract, store, and retrieve preferences within these categories. |
| Outcome: | The proposed system achieves an F1-score of .78 to .95 in preference extraction, depending on category granularity, and is suitable for industrial applications. |
CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty (2026.acl-long)
Copied to clipboard
| Challenge: | Existing benchmarks for Large Language Model (LLM) agents focus on task completion under idealistic settings but overlook reliability in real-world, user-facing applications. |
| Approach: | They propose a benchmark to evaluate consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. |
| Outcome: | The proposed benchmarks evaluate consistency, uncertainty handling, and capability awareness in multi-turn, tool-using LLM agents in an in-car assistant domain. |
Uncertainty Aware Review Hallucination for Science Article Classification (2021.findings-acl)
Copied to clipboard
Korbinian Friedl, Georgios Rizos, Lukas Stappen, Madina Hasan, Lucia Specia, Thomas Hain, Björn Schuller
| Challenge: | Existing approaches to peer review support are limited in their use of available information and subjectivity. |
| Approach: | They propose to use aleatory uncertainty and loss importance interpolations to model review representations at test time to provide a realistic evaluation framework. |
| Outcome: | The proposed framework makes better use of the available information and is realistic with respect to the limitations set by the task 1 . |