MDAgents: A Dynamic Multi-Agent Framework for Improving Medical Decision Making with Large Language Models
Base models show promise in medicine, especially for assisting in complex tasks such as medical decision making (MDM). MDM is ...
Tag: language
Leopard: A multimodal large language model (MLLM) designed specifically to handle vision and language tasks involving multiple text-rich images
In recent years, multimodal large language models (MLLM) have revolutionized vision-language tasks, improving capabilities such as image captioning and object ...
Multi-scale geometric analysis of language model features: from atomic patterns to galaxy structures
Large language models (LLMs) have become powerful tools in natural language processing, but understanding their internal representations remains a major ...
Computational bottlenecks in training large, small-scale language models
This paper was accepted into the Efficient Natural Speech and Language Processing (ENLSP) workshop at the NeurIPS 2024 Workshop. While ...
WACK: Advancing Hallucination Detection by Identifying Knowledge-Based Errors in Language Models via High-Precision Data Sets and Model-Specific Cueing Techniques
Large language models (LLMs) are widely used in natural language tasks, from question answering to conversational ai. However, a persistent ...
Improving task scheduling in linguistic agents: Leveraging graph neural networks to improve task decomposition and decision making in large language models
Task planning in linguistic agents is gaining attention in LLM research, focusing on breaking down complex tasks into manageable subtasks ...
Relaxed Recursive Transformers with Layered Low-Rank Adaptation: Achieving High Performance and Low Computational Cost on Large Language Models
Large language models (LLMs) are based on deep learning architectures that capture complex linguistic relationships within layered structures. Primarily based ...
This AI article explores how large language model additions improve adaptability in predictive modeling for changing tabular data environments
Machine learning for predictive modeling aims to accurately forecast outcomes based on input data. One of the main challenges in ...
Hierarchical Coding for mRNA Language Modeling (HELM): A Novel Pre-Training Strategy Incorporating Codon-Level Hierarchical Structure into Language Model Training
Messenger RNA (mRNA) plays a crucial role in protein synthesis, translating genetic information into proteins through a process involving sequences ...
ConvKGYarn: Configurable and Scalable Conversational Knowledge Graph QA Datasets with Large Language Models
The rapid evolution of large language models (LLMs) and conversational assistants requires dynamic, scalable, and configurable conversational datasets for training ...
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