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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.12495 |
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| _version_ | 1866914243841884160 |
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| author | Tatarinov, Nikita Kannan, Vidhyakshaya Srinivasa, Haricharana Raj, Arnav Anand, Harpreet Singh Singh, Varun Luthra, Aditya Lade, Ravij Shah, Agam Chava, Sudheer |
| author_facet | Tatarinov, Nikita Kannan, Vidhyakshaya Srinivasa, Haricharana Raj, Arnav Anand, Harpreet Singh Singh, Varun Luthra, Aditya Lade, Ravij Shah, Agam Chava, Sudheer |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_12495 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation Tatarinov, Nikita Kannan, Vidhyakshaya Srinivasa, Haricharana Raj, Arnav Anand, Harpreet Singh Singh, Varun Luthra, Aditya Lade, Ravij Shah, Agam Chava, Sudheer Computation and Language 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. |
| title | KG-MuLQA: A Framework for KG-based Multi-Level QA Extraction and Long-Context LLM Evaluation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2505.12495 |