Large language models for generative information extraction: A survey
Abstract Information Extraction (IE) aims to extract structural knowledge from plain natural
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …
language texts. Recently, generative Large Language Models (LLMs) have demonstrated …
The TRIPOD-LLM reporting guideline for studies using large language models
Large language models (LLMs) are rapidly being adopted in healthcare, necessitating
standardized reporting guidelines. We present transparent reporting of a multivariable …
standardized reporting guidelines. We present transparent reporting of a multivariable …
Large language models in biomedical natural language processing: benchmarks, baselines, and recommendations
Biomedical literature is growing rapidly, making it challenging to curate and extract
knowledge manually. Biomedical natural language processing (BioNLP) techniques that …
knowledge manually. Biomedical natural language processing (BioNLP) techniques that …
HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools
Motivation With the exponential growth of the life sciences literature, biomedical text mining
(BTM) has become an essential technology for accelerating the extraction of insights from …
(BTM) has become an essential technology for accelerating the extraction of insights from …
Ratescore: A metric for radiology report generation
This paper introduces a novel, entity-aware metric, termed as Radiological Report (Text)
Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models …
Evaluation (RaTEScore), to assess the quality of medical reports generated by AI models …
Large Language Model-Based Natural Language Encoding Could Be All You Need for Drug Biomedical Association Prediction
Analyzing drug-related interactions in the field of biomedicine has been a critical aspect of
drug discovery and development. While various artificial intelligence (AI)-based tools have …
drug discovery and development. While various artificial intelligence (AI)-based tools have …
A perspective for adapting generalist ai to specialized medical ai applications and their challenges
The integration of Large Language Models (LLMs) into medical applications has sparked
widespread interest across the healthcare industry, from drug discovery and development to …
widespread interest across the healthcare industry, from drug discovery and development to …
[HTML][HTML] Artificial intelligence-based data extraction for next generation risk assessment: Is fine-tuning of a large language model worth the effort?
A Sonnenburg, B van der Lugt, J Rehn, P Wittkowski… - Toxicology, 2024 - Elsevier
To underpin scientific evaluations of chemical risks, agencies such as the European Food
Safety Authority (EFSA) heavily rely on the outcome of systematic reviews, which currently …
Safety Authority (EFSA) heavily rely on the outcome of systematic reviews, which currently …
Utsa-nlp at chemotimelines 2024: Evaluating instruction-tuned language models for temporal relation extraction
This paper presents our approach for the 2024 ChemoTimelines shared task. Specifically,
we explored using Large Language Models (LLMs) for temporal relation extraction. We …
we explored using Large Language Models (LLMs) for temporal relation extraction. We …
A novel large-language-model-driven framework for named entity recognition
Z Wang, H Chen, G Xu, M Ren - Information Processing & Management, 2025 - Elsevier
Named entity recognition (NER) stands as the foundational pillar of knowledge graphs
across multiple domains. Despite progress in NER using large language models (LLMs) …
across multiple domains. Despite progress in NER using large language models (LLMs) …