14 citations · 40 across the 12 of their papers we have counts for
12 papers
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,…
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…
Do Instruction-Tuned Models Always Perform Better Than Base Models? Evidence from Math and Domain-Shifted Benchmarks
Prateek Munjal, Clement Christophe, Ronnie Rajan +1
Instruction finetuning is standard practice for improving LLM performance, yet it remains unclear whether it enhances reasoning or merely induces surface-level pattern matching. We…
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…
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…
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…
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…
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…
MEDIC: Comprehensive Evaluation of Leading Indicators for LLM Safety and Utility in Clinical Applications
Praveenkumar Kanithi, Clément Christophe, Marco AF Pimentel +8
While Large Language Models (LLMs) achieve superhuman performance on standardized medical licensing exams, these static benchmarks have become saturated and increasingly disconnect…
Med42-v2: A Suite of Clinical LLMs
Clément Christophe, Praveen K Kanithi, Tathagata Raha +2
Med42-v2 introduces a suite of clinical large language models (LLMs) designed to address the limitations of generic models in healthcare settings. These models are built on Llama3…
Beyond Metrics: A Critical Analysis of the Variability in Large Language Model Evaluation Frameworks
Marco AF Pimentel, Clément Christophe, Tathagata Raha +3
As large language models (LLMs) continue to evolve, the need for robust and standardized evaluation benchmarks becomes paramount. Evaluating the performance of these models is a co…
Med42 -- Evaluating Fine-Tuning Strategies for Medical LLMs: Full-Parameter vs. Parameter-Efficient Approaches
Clément Christophe, Praveen K Kanithi, Prateek Munjal +13
This study presents a comprehensive analysis and comparison of two predominant fine-tuning methodologies - full-parameter fine-tuning and parameter-efficient tuning - within the co…