3 papers
cs.CL2026
FactCorrector: A Graph-Inspired Approach to Long-Form Factuality Correction of Large Language Models
Javier Carnerero-Cano, Massimiliano Pronesti, Radu Marinescu +6
Large language models (LLMs) are widely used in knowledge-intensive applications but often generate factually incorrect responses. A promising approach to rectify these flaws is co…
cs.LG2025
Optimistic Exploration for Risk-Averse Constrained Reinforcement Learning
James McCarthy, Radu Marinescu, Elizabeth Daly +1
Risk-averse Constrained Reinforcement Learning (RaCRL) aims to learn policies that minimise the likelihood of rare and catastrophic constraint violations caused by an environment's…
cs.CL2025
FactReasoner: A Probabilistic Approach to Long-Form Factuality Assessment for Large Language Models
Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee +5
Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs, thus limiting their…