Paper Red-Teaming Large Language Models using Chain of Utterances for Safety-Alignment Larger language models (LLMs) have taken the world by storm with their massive multi-tasking capabilities simply by optimizing over a next-word prediction objective. With the emergence of their properties and ...
In this paper, we aim to systematically investigate the capabilities of GPT-4o in addressing 10 low-level data analysis tasks. Our study seeks to answer the following critical questions, shedding light on the potential of MLLMs in performing detailed, granular analyses. ...
Development Roadmap (2024 Q3) Please cite our paper,SGLang: Efficient Execution of Structured Language Model Programs, if you find the project useful. We also learned from the design and reused code from the following projects:Guidance,vLLM,LightLLM,FlashInfer,Outlines, andLMQL. ...
In this paper, we revisit the task of offensive language identification for low-resource languages. Our work focuses on Sinhala, an Indo-Aryan language spoken by over 17 million people in Sri Lanka. Sinhala is one of the two official languages in Sri Lanka. Most of the people who speak Sin...
ReadPaper是粤港澳大湾区数字经济研究院推出的专业论文阅读平台和学术交流社区,收录近2亿篇论文、近2.7亿位科研论文作者、近3万所高校及研究机构,包括nature、science、cell、pnas、pubmed、arxiv、acl、cvpr等知名期刊会议,涵盖了数学、物理、化学、材料、金融、计算机
ReadPaper是深圳学海云帆科技有限公司推出的专业论文阅读平台和学术交流社区,收录近2亿篇论文、近2.7亿位科研论文作者、近3万所高校及研究机构,包括nature、science、cell、pnas、pubmed、arxiv、acl、cvpr等知名期刊会议,涵盖了数学、物理、化学、材料、金融、计算机科
The study is part of a U.S.-based research project which applied the DCM methodology to the reading domain of a large-scale assessment. This paper showcases how the reading construct in a K–12 ELP assessment was tackled within the DCM framework. The paper explicates a methodology forQ-...
Code for reproducing the ACL'23 paper: Don't Generate, Discriminate: A Proposal for Grounding Language Models to Real-World Environments - GitHub - dki-lab/Pangu: Code for reproducing the ACL'23 paper: Don't Generate, Discriminate: A Proposal for Ground
Development Roadmap (2024 Q3) Citation And Acknowledgment Please cite our paper, SGLang: Efficient Execution of Structured Language Model Programs, if you find the project useful. We also learned from the design and reused code from the following projects: Guidance, vLLM, LightLLM, FlashInfer, ...