微軟的AI研究團隊近日發表了一份名為 SpreadsheetLLM的研究報告 ，旨在協助大型語言模型（LLM）更容易理解試算表（Spreadsheet）中的資料，以便正確推論。
試算表由許多儲存格組成，這些儲存格存放著文字、數字、公式或函數，嵌入在許多的行與
OpenAI launched GPT-2 in 2019, reportedly costing $256 per hour to train. However, it’s been five years since then, and we’re already at GPT-4o. Advancements in hardware, software, and data mean that training the same model will take less time and less money, as Andrej Karpathy , the developer behind the project to reproduce GPT-2 in llm.c, has proven.
The primary driver of cost savings
Too Real: Microsoft has developed a new iteration of its neural codec language model, Vall-E, that surpasses previous efforts in terms of naturalness, speech robustness, and speaker similarity. It is the first of its kind to reach human parity in a pair of popular benchmarks, and is apparently so lifelike that Microsoft has no plans to grant access to the public.
Leveraging Vall-E'
大廠持續投入終端裝置上的AI模型開發。Llama模型家族獲得眾多開發人員使用後， Meta本週稍早又公佈 可在行動裝置上執行，參數量不到10億的新AI模型家族。
由於在雲端執行上百甚至上千億參數的大型語言模型（LLM）增加雲端運
