We introduce KG-MuLQA (Knowledge-Graph-based Multi-Level Question-Answer Extraction): a framework that (1) extracts QA pairs at multiple complexity levels (2) along three key dimensions -- multi-hop retrieval, set operations, and answer plurality, (3) by leveraging knowledge-graph-based document representations. This approach enables fine-grained assessment of model performance across controlled difficulty levels. Using this framework, we construct a dataset of 20,139 QA pairs based on financial credit agreements and evaluate 16 proprietary and open-weight Large Language Models, observing that even the best-performing models struggle with set-based comparisons and multi-hop reasoning over long contexts. Our analysis reveals systematic failure modes tied to semantic misinterpretation and inability to handle implicit relations.
@article{arxiv.2505.12495,
title = {KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation},
author = {Nikita Tatarinov and Vidhyakshaya Kannan and Haricharana Srinivasa and Arnav Raj and Harpreet Singh Anand and Varun Singh and Aditya Luthra and Ravij Lade and Agam Shah and Sudheer Chava},
journal= {arXiv preprint arXiv:2505.12495},
year = {2026}
}