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Efficient continual pre-training LLMs for financial domains

AWS Machine Learning

For example, the training data used for BloombergGPT is 51% domain-specific documents, including financial news, filings, and other financial materials. The authors of BloombergGPT concluded that their model outperforms all other models tested for four of the five financial tasks.

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The Importance of Employee Loyalty in the Workplace

InMoment XI

In a 2016 post Talkdesk , a provider of call center solutions, reported the yearly turnover as 30-45%, double or triple that of all U.S. Then we’d better get a hell of a lot better,” Lorne Rubis told the Financial Post. industries in 2013.). Change How You Account for Customer Service. Solicit Employee Feedback.

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Improving the Customer Experience in Financial Services

Forcivity Salesforce

Customer experience management in financial services is becoming more and more critical to success for the industry. Changing customer expectations and evolving government regulations have triggered a period of transformation for financial service providers. The importance of CX in Financial Services.

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Twin Cities Business Journal 2019 Chief Financial Officer (CFO) of the Year—Calabrio’s Jenny Kray

Calabrio

She had to do it while also accelerating Calabrio’s transition to a SaaS business model—a decision with significant repercussions on financial forecasting and reporting, and product and business decisions. The post Twin Cities Business Journal 2019 Chief Financial Officer (CFO) of the Year—Calabrio’s Jenny Kray appeared first on Calabrio.

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Why 2016 should NOT be ‘the year of the customer’

ijgolding

Some were financially related. Think about it this way – if 2016 is to be the year of the customer, what does that mean for the customer in 2017? If 2016 is the year of the customer, does that mean we do not have to care about the customer any other year? Some resolutions were related to personal health and well-being.

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Financial text generation using a domain-adapted fine-tuned large language model in Amazon SageMaker JumpStart

AWS Machine Learning

In particular, we show how you can fine-tune the GPT-J 6B language model for financial text generation using both the JumpStart SDK and Amazon SageMaker Studio UI on a publicly available dataset of SEC filings. We use financial text from SEC filings to fine-tune a GPT-J 6B LLM for financial applications.

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Domain-adaptation Fine-tuning of Foundation Models in Amazon SageMaker JumpStart on Financial data

AWS Machine Learning

In particular, we show how you can fine-tune the GPT-J 6B language model for financial text generation using both the JumpStart SDK and Amazon SageMaker Studio UI on a publicly available dataset of SEC filings. We use financial text from SEC filings to fine-tune a GPT-J 6B LLM for financial applications.