LLMs as Master Forgers: Generating Synthetic Time Series Data for Manufacturing
This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes.
Emelitere kwa ụbọchị1797 ezi akụkọ
Mkpuchi AI nyochara isi mmalite nke mmalite ngwaahịa, mgbanwe amụma, nyocha nchekwa, na mmegharị ụlọ ọrụ, nke otu agụmakwụkwọ na-anaghị akwụ ụgwọ kọwara n'asụsụ bekee dị larịị.
Akụkọ ọ bụla na-ejikọta na ihe akaebe siri ike dị: isi mmalite mgbe ọ dị, ma ọ bụghị nke akọwapụtara nke ọma.
Gịnị mere, ihe mere o ji dị mkpa, na ihe na-ekiri - na-enweghị jargon.
Mgbe mgbaàmà ahụ dị gịrịgịrị, anyị na-ebipụta ihe ọ bụla kama ịkwanye ndepụta.
Akụkọ AI nyochara isi mmalite, nke kachasị ọhụrụ, maka ndị chọrọ ịghọta AI na-achụghị hype.
This paper presents a novel framework leveraging Large Language Models (LLMs) to generate synthetic time series data for manufacturing processes.
Researchers propose style-debiased direct preference optimization (SD-DPO) to improve the accuracy of large language models (LLMs) in retrieving stored knowledge.
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