Papers by Evangelos E. Papalexakis
ExpertGenQA: Open-ended QA generation in Specialized Domains (2025.findings-emnlp)
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| Challenge: | Existing methods for generating high-quality question–answer (QA) pairs yield generic or shallow questions that fail to reflect the depth and structure of expert-written examples. |
| Approach: | They propose a question-answer generation protocol that combines few-shot prompting with dual categorization by topic and question style to produce more diverse and cognitively meaningful QA pairs. |
| Outcome: | The proposed protocol achieves twice the efficiency of standard few-shot methods while maintaining 94.4% topic coverage. |
Every Response Counts: Quantifying Uncertainty of LLM-based Multi-Agent Systems through Tensor Decomposition (2026.acl-long)
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| Challenge: | Existing methods for MAS fail to address the unique complexities of multi-step reasoning . Existing uncertainty quantification methods struggle with cascading uncertainty . |
| Approach: | They propose a framework that quantifies uncertainty through tensor decomposition . they show that MATU effectively estimates holistic and robust uncertainty . |
| Outcome: | The proposed framework disentangles and quantifies distinct sources of uncertainty . it is generalizable across different agent structures and can be used for scientific discovery, education, healthcare and transportation. |
Uncertainty Quantification of Large Language Models through Multiple Uncertainty Sources (2026.findings-acl)
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| Challenge: | Existing methods for uncertainty quantification fail to capture multifaceted nature of natural language generation. |
| Approach: | They propose a multi-resource Uncertainty Quantification framework that integrates heterogeneous uncertainty signals into a unified measure. |
| Outcome: | The proposed framework outperforms existing methods on CoQA, NQ_Open, and HotpotQA. |