5 citations · 11 across the 5 of their papers we have counts for
5 papers
AgentQuest: A Modular Benchmark Framework to Measure Progress and Improve LLM Agents
Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito +4
The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks. As with any research pursuit, benchmar…
Linking Surface Facts to Large-Scale Knowledge Graphs
Gorjan Radevski, Kiril Gashteovski, Chia-Chien Hung +2
Open Information Extraction (OIE) methods extract facts from natural language text in the form of ("subject"; "relation"; "object") triples. These facts are, however, merely surfac…
KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models
Haris Widjaja, Kiril Gashteovski, Wiem Ben Rim +5
Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) task…
Human-Centric Research for NLP: Towards a Definition and Guiding Questions
Bhushan Kotnis, Kiril Gashteovski, Julia Gastinger +7
With Human-Centric Research (HCR) we can steer research activities so that the research outcome is beneficial for human stakeholders, such as end users. But what exactly makes rese…
A Human-Centric Assessment Framework for AI
Sascha Saralajew, Ammar Shaker, Zhao Xu +5
With the rise of AI systems in real-world applications comes the need for reliable and trustworthy AI. An essential aspect of this are explainable AI systems. However, there is no…