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关于建立基准的教程用于使用公共数据评估商业大型语言模型.pdf

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1、IMF IMF|Statistics1Benchmarking Commercial Large Language ModelsNOVEMBER 21,2024Jan BatznerWeizenbaum Institute,German Internet InstituteTechnical University Munich,Grad.Center CITIMF IMF|StatisticsIMF IMF|Statistics3Research on Research on Large Language Models Large Language Models“Since large lan

2、guage models,or LLMs,started to appear in 2017,the share of AI content in patent applications related to algorithmictrading has risen from 19 percent in 2017 to over 50 percent eachyear since 2020,suggesting a wave of innovation is coming in thisarea.”(IMF Blog,2024)Nassira Abbas,Charles Cohen,Dirk

3、Jan Grolleman,Benjamin Mosk(2024):Artificial Intelligence Can Make Markets More Efficientand More Volatile.International Monetary Fund(IMF)Blog.EconPhenomenonIMF IMF|Statistics4Research on Research on Large Language Models Large Language Models“Since large language models,or LLMs,started to appear i

4、n 2017,the share of AI content in patent applications related to algorithmic trading has risen from 19 percent in 2017 to over 50 percent each year since 2020,suggesting a wave of innovation is coming in this area.”(IMF Blog,2024)Nassira Abbas,Charles Cohen,Dirk Jan Grolleman,Benjamin Mosk(2024):Art

5、ificial Intelligence Can Make Markets More Efficientand More Volatile.International Monetary Fund(IMF)Blog.EconNLPAnthropic.Claude 3 Models on B IMF|Statistics5Language Modeling:What is a LM?Language Modeling:What is a LM?probability distribution over sequences of words LMs are generative models:Wha

6、t we understand as LLM are Autoregressive(AR)language models:Yann Dubois(2024):CS229 Building Large Language Models(LLMs).Stanford Online Lecture.Recorded Lecture.Slide 5(Credit:Y.Dubois).LMIMF IMF|Statistics6ReinforReinforcecement Learning by Human Feedback(RLHF)ment Learning by Human Feedback(RLHF

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本文主要探讨了大型语言模型(LLM)在算法交易领域的应用及其对市场效率和波动性的影响。根据国际货币基金组织(IMF)博客的数据,自2017年大型语言模型出现以来,与算法交易相关的专利申请中AI内容的比例从2017年的19%上升到2020年以来的每年超过50%,预示着这一领域即将迎来一波创新浪潮。文章还提到了Anthropic公司在其网站上发布的关于Claude 3模型的基准测试结果。此外,文章探讨了LLM基准测试的知识来源和存在的问题,如过度依赖维基百科等。同时,文章还以德国政治为例,研究了商业大型语言模型在政治偏见和拍马屁方面的表现。最后,文章提出了构建LLM基准测试的一些研究问题和数据来源,包括学术测试、百科全书知识、专家调查、调查数据、投票建议应用程序和访谈数据等。
"AI在算法交易中的专利申请占比如何变化?" "大型语言模型如何影响市场效率和波动性?" "大型语言模型在政治偏见和顺从性方面的表现如何?"
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