Papers by Jasmina Gajcin
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. |
Synthetic Data for Evaluation: Supporting LLM-as-a-Judge Workflows with EvalAssist (2025.emnlp-demos)
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Martín Santillán Cooper, Zahra Ashktorab, Hyo Jin Do, Erik Miehling, Werner Geyer, Jasmina Gajcin, Elizabeth M. Daly, Qian Pan, Michael Desmond
| Challenge: | EvalAssist is a web-based application designed to assist human-centered evaluation of language model outputs. |
| Approach: | They propose a synthetic data generation tool integrated into EvalAssist to assist human-centered evaluation of language model outputs. |
| Outcome: | The proposed tool supports flexible prompting, RAG-based grounding, persona diversity, and iterative generation workflows. |