activity
20242026
most citedMed42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches

14 citations · 40 across the 10 of their papers we have counts for

collaborators

10 papers

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.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.LG20251 cited

Gene42: Long-Range Genomic Foundation Model With Dense Attention

Kirill Vishniakov, Boulbaba Ben Amor, Engin Tekin +12

We introduce Gene42, a novel family of Genomic Foundation Models (GFMs) designed to manage context lengths of up to 192,000 base pairs (bp) at a single-nucleotide resolution. Gene4…

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…