Retrieval-augmented generation for ai-generated content: A survey
The development of Artificial Intelligence Generated Content (AIGC) has been facilitated by
advancements in model algorithms, scalable foundation model architectures, and the …
advancements in model algorithms, scalable foundation model architectures, and the …
Preventing harm from non-conscious bias in medical generative AI
J Hastings - The Lancet Digital Health, 2024 - thelancet.com
Large language models such as OpenAI's GPT-4 have the potential to transform medicine1
by enabling automation of a range of tasks, including writing discharge summaries, 2 …
by enabling automation of a range of tasks, including writing discharge summaries, 2 …
Ehragent: Code empowers large language models for complex tabular reasoning on electronic health records
Large language models (LLMs) have demonstrated exceptional capabilities in planning and
tool utilization as autonomous agents, but few have been developed for medical problem …
tool utilization as autonomous agents, but few have been developed for medical problem …
Bmretriever: Tuning large language models as better biomedical text retrievers
Developing effective biomedical retrieval models is important for excelling at knowledge-
intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly …
intensive biomedical tasks but still challenging due to the deficiency of sufficient publicly …
Ram-ehr: Retrieval augmentation meets clinical predictions on electronic health records
We present RAM-EHR, a Retrieval AugMentation pipeline to improve clinical predictions on
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
Electronic Health Records (EHRs). RAM-EHR first collects multiple knowledge sources …
Polyie: A dataset of information extraction from polymer material scientific literature
Scientific information extraction (SciIE), which aims to automatically extract information from
scientific literature, is becoming more important than ever. However, there are no existing …
scientific literature, is becoming more important than ever. However, there are no existing …
Arl2: Aligning retrievers for black-box large language models via self-guided adaptive relevance labeling
Retrieval-augmented generation enhances large language models (LLMs) by incorporating
relevant information from external knowledge sources. This enables LLMs to adapt to …
relevant information from external knowledge sources. This enables LLMs to adapt to …
HYDRA: Model Factorization Framework for Black-Box LLM Personalization
Personalization has emerged as a critical research area in modern intelligent systems,
focusing on mining users' behavioral history and adapting to their preferences for delivering …
focusing on mining users' behavioral history and adapting to their preferences for delivering …
MedAdapter: Efficient Test-Time Adaptation of Large Language Models towards Medical Reasoning
Despite their improved capabilities in generation and reasoning, adapting large language
models (LLMs) to the biomedical domain remains challenging due to their immense size …
models (LLMs) to the biomedical domain remains challenging due to their immense size …
Enhancing large language models through external domain knowledge
L Welz, C Lanquillon - International Conference on Human-Computer …, 2024 - Springer
Abstract Large Language Models (LLM) demonstrate promising results in generating
content with current fine-tuning and prompting methods. Yet, they have limited application in …
content with current fine-tuning and prompting methods. Yet, they have limited application in …