6 papers
AutoForest: Automatically Generating Forest Plots from Biomedical Studies with End-to-End Evidence Extraction and Synthesis
Massimiliano Pronesti, Angelo Miculescu, Mohsin Kapdi +8
Systematic reviews rely on forest plots to synthesise quantitative evidence across biomedical studies, but generating them remains a fragmented and labour-intensive process. Resear…
Beyond Outcome Verification: Verifiable Process Reward Models for Structured Reasoning
Massimiliano Pronesti, Anya Belz, Yufang Hou
Recent work on reinforcement learning with verifiable rewards (RLVR) has shown that large language models (LLMs) can be substantially improved using outcome-level verification sign…
Enhancing Study-Level Inference from Clinical Trial Papers via Reinforcement Learning-Based Numeric Reasoning
Massimiliano Pronesti, Michela Lorandi, Paul Flanagan +3
Systematic reviews in medicine play a critical role in evidence-based decision-making by aggregating findings from multiple studies. A central bottleneck in automating this process…
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…
Secure Change-Point Detection for Time Series under Homomorphic Encryption
Federico Mazzone, Giorgio Micali, Massimiliano Pronesti
We introduce the first method for change-point detection on encrypted time series. Our approach employs the CKKS homomorphic encryption scheme to detect shifts in statistical prope…
Query-driven Document-level Scientific Evidence Extraction from Biomedical Studies
Massimiliano Pronesti, Joao Bettencourt-Silva, Paul Flanagan +4
Extracting scientific evidence from biomedical studies for clinical research questions (e.g., Does stem cell transplantation improve quality of life in patients with medically refr…