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Amin Dada

4 accepted papers

2025

A Modular Approach for Clinical SLMs Driven by Synthetic Data with Pre-Instruction Tuning, Model Merging, and Clinical-Tasks Alignment

ACL 2025long

High computation costs and latency of large language models such as GPT-4 have limited their deployment in clinical settings. Small language models (SLMs) offer a cost-effective alternative, but their limited capacity requires biomedical domain adaptation, which remains challenging. An additional bo…

Cited by 0SourcePDFScholar
2025

Towards Conditioning Clinical Text Generation for User Control

ACL 2025finding

Deploying natural language generation systems in clinical settings remains challenging despite advances in Large Language Models (LLMs), which continue to exhibit hallucinations and factual inconsistencies, necessitating human oversight. This paper explores automated dataset augmentation using LLMs…

2024

Comprehensive Study on German Language Models for Clinical and Biomedical Text Understanding

COLING 2024main

Recent advances in natural language processing (NLP) can be largely attributed to the advent of pre-trained language models such as BERT and RoBERTa. While these models demonstrate remarkable performance on general datasets, they can struggle in specialized domains such as medicine, where unique dom…

Cited by 7SourcePDFScholar
2023

On the Impact of Cross-Domain Data on German Language Models

EMNLP 2023long findings

Traditionally, large language models have been either trained on general web crawls or domain-specific data. However, recent successes of generative large language models, have shed light on the benefits of cross-domain datasets. To examine the significance of prioritizing data diversity over qualit…

Cited by 0SourceScholar