Large Language Models (LLMs) are transformer‐based neural networks with billions of parameters trained on very large text corpora from diverse sources. LLMs have the potential to improve healthcare due to their capability to parse complex concepts and generate context‐based responses. The inter...
The emerging large language models (LLMs) are considered to be potential on MEE. However, such methods have been shown to encounter issues related to accuracy, interpretability, and generalizability. In this paper, we propose OptimalMEE to optimize LLMs for MEE through fine-tuning and post-hoc...
Abstract 4144884: Optimizing Large Language Models for Interpreting American Heart Association Dietary Guidelines in Nutrition Education for Cardiovascular Disease Prevention: A Retrieval-Augmented Generation Framework doi:10.1161/circ.150.suppl_1.4144884Artificial IntelligenceNutritionGuidelinesPreventionEvidence-based ...
Day5-lec6-1-读Prefix-Tuning: Optimizing Continuous Prompts for Generation COS 597G:Understanding Large Language Models 著名的ptuning,至今仍有一席之地。值得一读。 1.动机 由于transformer太大了,全量微调太耗资源。所以提出了基于prefix的轻量级微调策略。 如果我没记错的话,全量微调主要是耗费显存。如果1张...
ChatGPT: Optimizing Language Models for Dialogue Abstract We’ve trained a model called ChatGPT which interacts in a conversational way. The dialogue format makes it possible for ChatGPT to answer followup questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests....
We evaluated the inference performance of the new Azure ND H200 v5-series for Small Language Models (SLMs) and Large Language Models (LLMs). The ND H200 v5-series, powered by eight NVIDIA H200 Tensor Core GPUs, offers a 76% increase in memory bandwidth over the NVIDIA H100 Tensor Cor...
Given the semantic capabilities of large language models, we address these problems using a reinforcement learning (RL) formulation where large language models provide feedback for the novel items. However, given millions of candidate items, the sample complexity of a standard RL algorithm can be ...
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance, "generate-then-read" pipeline is proposed to replace the retrieval stage with generation from the LLM itself. Although ...
Temporal Scaling Law for Large Language Models Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Jianwei Niu, Guiguang Ding 2024 nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales ...
paper:MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases Link:https://arxiv.org/pdf/2402.14905 TL,DR: 适合mobile设备上用的LM模型架构的探索,并提出了MobileLLM。 端侧设备的特点 常见的端侧设备基本都是memory+算力 有限,因此需要训一些参数量比较少的语言模型。