Papers by Adam Dejl
Comprehensiveness Metrics for Automatic Evaluation of Factual Recall in Text Generation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) produce incomplete or selectively omit key information . omissions of key information or misrepresentation of conflicting evidence can cause harm . |
| Approach: | They propose a method that decomposes texts into atomic statements and uses natural language inference to identify missing facts and a Q A-based metric that extracts question-answer pairs and compares responses across sources. |
| Outcome: | The proposed evaluation metrics show they perform better than more complex metrics, but at a cost. |
EvalSense: A Framework for Domain-Specific LLM (Meta-)Evaluation (2026.eacl-demo)
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| Challenge: | EvalSense is a flexible framework for constructing domain-specific evaluation suites for large language models . it provides out-of-the-box support for a broad range of model providers and evaluation strategies . |
| Approach: | They propose a framework for constructing domain-specific evaluation suites for large language models. |
| Outcome: | The proposed framework provides out-of-the-box support for a broad range of model providers and evaluation strategies. |
Evaluating Uncertainty Quantification Methods in Argumentative Large Language Models (2025.findings-emnlp)
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| Challenge: | ArgLLMs are an explainable LLM framework for decision-making based on computational argumentation in which uncertainty quantification plays a critical role. |
| Approach: | They propose to integrate LLM UQ methods into argumentative LLMs to evaluate their performance on claim verification tasks. |
| Outcome: | The proposed method outperforms more complex approaches on claim verification tasks. |