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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
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