D-NLP at SemEval-2024 Task 2: Evaluating Clinical Inference Capabilities of Large Language Models

arXiv:2405.04170v1 Announce Type: new
Abstract: Large language models (LLMs) have garnered significant attention and widespread usage due to their impressive performance in various tasks. However, they are not without their own set of challenges, including issues such as hallucinations, factual inconsistencies, and limitations in numerical-quantitative reasoning. Evaluating LLMs in miscellaneous reasoning tasks remains an active area of research. Prior to the breakthrough of LLMs, Transformers had already proven successful in the medical domain, effectively employed for various natural language understanding …

abstract arxiv attention capabilities challenges clinical cs.cl hallucinations however inference language language models large language large language models limitations llms nlp numerical performance quantitative quantitative reasoning reasoning set tasks type usage