Papers by Edgar Meij
Evaluating the Calibration of Knowledge Graph Embeddings for Trustworthy Link Prediction (2020.emnlp-main)
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| Challenge: | Existing calibration techniques are less effective under the standard closed-world assumption (CWA) and the more realistic open-world hypothesis (OWA) Existing methods are not effective under OWA and provide explanations for this discrepancy. |
| Approach: | They conduct an evaluation under the standard closed-world assumption (CWA) and introduce the more realistic but challenging open-world assume (OWA) . they find existing calibration techniques are much less effective under the OWA than the CWA . |
| Outcome: | The proposed calibration techniques are much less effective under the open-world assumption (OWA) and explain the discrepancy. |
Beyond Static Toolsets: Self-Evolving LLM Tool Agents via Continual Documentation Adaptation (2026.findings-acl)
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| Challenge: | Existing methods for evaluating tool usage assume static toolsets with fixed APIs and documentation. |
| Approach: | They propose a continual documentation adaptation framework that allows LLM agents to self-evolve by updating tool documentation. |
| Outcome: | The proposed framework improves performance on three evolution patterns on dynamic extensions of StableToolBench and RestBench. |
Improving Dialogue State Tracking with Turn-based Loss Function and Sequential Data Augmentation (2021.findings-emnlp)
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| Challenge: | Existing models rely on a traditional cross-entropy loss function during training, which may not be optimal for improving the joint goal accuracy. |
| Approach: | They propose a Turn-based Loss Function that penalises the model if it inaccurately predicts a slot value at the early turns more so than in later turns to improve joint goal accuracy. |
| Outcome: | The proposed techniques improve the state-of-the-art model by approximately 7-8% relative reduction in error and achieve a new state- of-the art joint goal accuracy with 59.50 and 54.90 on MultiWOZ2.1 and MultiWOz2.2, respectively. |
A Joint Optimization Framework for Enhancing Efficiency of Tool Utilization in LLM Agents (2025.findings-acl)
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| Challenge: | Existing efforts for tool utilization involve an LLM agent that contains instructions on using the description of the available tools to determine and call the tools required to solve the problem. |
| Approach: | They propose to optimize the context of LLM agents by combining the instructions provided in agent prompts and tool descriptions to enhance their interaction. |
| Outcome: | The proposed framework improves both the instructions provided in agent prompt and tool description, enhancing their interaction. |