Remove AI Researcher Remove NLP Remove Prompt Engineering
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Microsoft Introduces Automatic Prompt Optimization Framework for LLMs

Analytics Vidhya

Microsoft AI Research has recently introduced a new framework called Automatic Prompt Optimization (APO) to significantly improve the performance of large language models (LLMs). This framework is designed to help users create better prompts with minimal manual intervention & optimize prompt engineering for better results.

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Shaping the Future of Artificial Intelligence AI: The Significance of Prompt Engineering for Progress and Innovation

Marktechpost

What is prompt engineering? For developing any GPT-3 application, it is important to have a proper training prompt along with its design and content. Prompt is the text fed to the Large Language Model. Prompt engineering involves designing a prompt for a satisfactory response from the model.

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5 Must-Have Skills to Get Into Prompt Engineering

ODSC - Open Data Science

Who hasn’t seen the news surrounding one of the latest jobs created by AI, that of prompt engineering ? If you’re unfamiliar, a prompt engineer is a specialist who can do everything from designing to fine-tuning prompts for AI models, thus making them more efficient and accurate in generating human-like text.

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A New AI Research Introduces Directional Stimulus Prompting (DSP): A New Prompting Framework to Better Guide the LLM in Generating the Desired Summary

Marktechpost

Natural language processing (NLP) has seen a paradigm shift in recent years, with the advent of Large Language Models (LLMs) that outperform formerly relatively tiny Language Models (LMs) like GPT-2 and T5 Raffel et al. on a variety of NLP tasks. All Credit For This Research Goes To the Researchers on This Project.

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Microsoft AI Research Open-Sources PromptWizard: A Feedback-Driven AI Framework for Efficient and Scalable LLM Prompt Optimization

Marktechpost

Despite their importance, prompt creation is a labor-intensive process that often requires domain-specific knowledge and significant human effort. These limitations have spurred the development of automated systems to refine and optimize prompts efficiently. Trending: LG AI Research Releases EXAONE 3.5:

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Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI…

ODSC - Open Data Science

Evolving Trends in Prompt Engineering for Large Language Models (LLMs) with Built-in Responsible AI Practices Editor’s note: Jayachandran Ramachandran and Rohit Sroch are speakers for ODSC APAC this August 22–23. He is responsible for Applied AI research, Innovation, and IP development.

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Stanford and Cornell Researchers Introduce Tart: An Innovative Plug-and-Play Transformer Module Enhancing AI Reasoning Capabilities in a Task-Agnostic Manner

Flipboard

Surprisingly, most methods for narrowing the performance gap, such as prompt engineering and active example selection, only target the LLM’s learned representations. In contrast, their research examines an alternative strategy for enhancing LLM reasoning skills. across various NLP tasks.

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