Papers by Radu Marinescu
SIMBA UQ: Similarity-Based Aggregation for Uncertainty Quantification in Large Language Models (2025.findings-emnlp)
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Debarun Bhattacharjya, Balaji Ganesan, Junkyu Lee, Radu Marinescu, Katya Mirylenka, Michael Glass, Xiao Shou
| Challenge: | Uncertainty quantification (UQ) provides measures of uncertainty, such as an estimate of the confidence in an LLM’s generated output. |
| Approach: | They propose a black-box approach where consistency is used as a proxy for confidence in a model's output. |
| Outcome: | The proposed methods are primarily but not necessarily entirely black- box, with consistency between output and other sampled generations used as a proxy for confidence in its correctness. |
FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models (2026.acl-long)
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Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu, Tigran T. Tchrakian, James Barry, Jasmina Gajcin, Yufang Hou, Alessandra Pascale, Elizabeth M. Daly
| Challenge: | Large language models (LLMs) often produce factually incorrect responses. |
| Approach: | They propose a new method that adapts across domains without retraining and leverages structured feedback to generate a correction. |
| Outcome: | The proposed method outperforms baseline methods on a VELI5 dataset and several popular long-form factuality datasets. |
FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models (2025.findings-emnlp)
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Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran T. Tchrakian, Javier Carnerero-Cano, Yufang Hou, Elizabeth M. Daly, Alessandra Pascale
| Challenge: | Large language models often fail to ensure factual accuracy of outputs thus limiting reliability in real-world applications. |
| Approach: | They propose a neuro-symbolic based factuality assessment framework that employs probabilistic reasoning to evaluate the truthfulness of long-form generated responses. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt-based methods in factual accuracy and recall. |