all AI news
BLADE: Enhancing Black-box Large Language Models with Small Domain-Specific Models
March 28, 2024, 4:48 a.m. | Haitao Li, Qingyao Ai, Jia Chen, Qian Dong, Zhijing Wu, Yiqun Liu, Chong Chen, Qi Tian
cs.CL updates on arXiv.org arxiv.org
Abstract: Large Language Models (LLMs) like ChatGPT and GPT-4 are versatile and capable of addressing a diverse range of tasks. However, general LLMs, which are developed on open-domain data, may lack the domain-specific knowledge essential for tasks in vertical domains, such as legal, medical, etc. To address this issue, previous approaches either conduct continuous pre-training with domain-specific data or employ retrieval augmentation to support general LLMs. Unfortunately, these strategies are either cost-intensive or unreliable in practical …
abstract arxiv blade box chatgpt cs.cl data diverse domain domains etc general gpt gpt-4 however knowledge language language models large language large language models legal llms medical small tasks type
More from arxiv.org / cs.CL updates on arXiv.org
Benchmarking LLMs via Uncertainty Quantification
1 day, 22 hours ago |
arxiv.org
CARE: Extracting Experimental Findings From Clinical Literature
1 day, 22 hours ago |
arxiv.org
Jobs in AI, ML, Big Data
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Machine Learning Engineer (MLOps)
@ Promaton | Remote, Europe
AIML - Sr Machine Learning Engineer, Data and ML Innovation
@ Apple | Seattle, WA, United States
Senior Data Engineer
@ Palta | Palta Cyprus, Palta Warsaw, Palta remote