Learn to Refuse: Making Large Language Models More Controllable andReliable through Knowledge Scope Limitation and Refusal MechanismLang CaoUniversity of Illinois Urbana-ChampaignDepartment of Computer Sciencelangcao2@illinois.eduAbstractLarge language models (LLMs) have demon-strated impressive language under...
浙江大学,Decision-Making in Robotic Grasping with Large Language Models论文解读 摘要:最近在大型语言模型方面的进步突显了它们编码大量语义知识以支持长期自主决策的潜力,这使它们成为未来家庭助理机器人认知能力的有前景的解决方案。然而,尽管大型语言模型可以提供高层次的决策,但目前还没有统一的范式将它们与机器人的...
BioChatter: making large language models accessible for biomedical research. Credit: Karen Arnott/EMBL-EBI Large language models (LLMs) have transformed how many of us work, from supporting content creation and coding to improving search engines. However, the lack of transparency, reproducibility, an...
Large language models (LLMs) provide new possibilities for engaging and intelligent conversational systems. However, productionizing and managing these models and ensuring they work to your advantage can be challenging. Two key strategies that can help are RAG-workflows and NeMo Guardrails.Retrieval...
Inspired by the knowledge-driven nature of human driving, recent approaches explore the potential of large language models (LLMs) to improve understanding and decision-making in traffic scenarios. They find that the pretrain-finetune paradigm of LLMs on downstream data with the Chain-of-Thought ...
Model, Code & Data for the EMNLP'23 paper "Making Large Language Models Better Data Creators" - microsoft/llm-data-creation
Large Language Models (LLMs) like GPT-4, MedPaLM-2, and Med-Gemini achieve performance competitively with human experts across various medical benchmarks. However, they still face challenges in making professional diagnoses akin to physicians, particularly in efficiently gathering patient information an...
Making Large Language Models Better Planners with Reasoning-Decision AlignmentData-driven approaches for autonomous driving (AD) have been widely adopted in ... Z Huang,T Tang,S Chen,... 被引量: 0发表: 2024年 Planning with Qualitative Temporal Preferences In this paper, we address the problem...
The official repository for paper "LLMaAA: Making Large Language Models as Active Annotators" - ridiculouz/LLMaAA
Few-shot learning is a challenging task that requires language models to generalize from limited examples. Large language models like GPT-3 and PaLM have made impressive progress in this area, but they still face difficulties in reasoning tasks such as GSM8K, a benchmark for a...