most citedNamed Clinical Entity Recognition Benchmark

2 citations · 4 across the 3 of their papers we have counts for

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cs.CL2026

Cross-Examination Framework: A Task-Agnostic Diagnostic for Information Fidelity in Text-to-Text Generation

Tathagata Raha, Clement Christophe, Nada Saadi +4

Traditional metrics like BLEU and BERTScore fail to capture semantic fidelity in generative text-to-text tasks. We adapt the Cross-Examination Framework (CEF) for a reference-free,…

cs.CL2026

Overalignment in Frontier LLMs: An Empirical Study of Sycophantic Behaviour in Healthcare

Clément Christophe, Wadood Mohammed Abdul, Prateek Munjal +3

As LLMs are increasingly integrated into clinical workflows, their tendency for sycophancy, prioritizing user agreement over factual accuracy, poses significant risks to patient sa…

cs.CL2025

Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency

Svetlana Maslenkova, Clement Christophe, Marco AF Pimentel +7

Large language models offer transformative potential for healthcare, yet their responsible and equitable development depends critically on a deeper understanding of how training da…

cs.CL20251 cited

Bridging Language Barriers in Healthcare: A Study on Arabic LLMs

Nada Saadi, Tathagata Raha, Clément Christophe +3

This paper investigates the challenges of developing large language models (LLMs) proficient in both multilingual understanding and medical knowledge. We demonstrate that simply tr…

cs.CL20242 cited

Named Clinical Entity Recognition Benchmark

Wadood M Abdul, Marco AF Pimentel, Muhammad Umar Salman +6

This technical report introduces a Named Clinical Entity Recognition Benchmark for evaluating language models in healthcare, addressing the crucial natural language processing (NLP…

cs.CL20241 cited

Beyond Fine-tuning: Unleashing the Potential of Continuous Pretraining for Clinical LLMs

Clément Christophe, Tathagata Raha, Svetlana Maslenkova +4

Large Language Models (LLMs) have demonstrated significant potential in transforming clinical applications. In this study, we investigate the efficacy of four techniques in adaptin…