10X Your Prompt Quality

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PROMPT ENGINEERING

🌐 10X Prompt Quality: Microsoft's Breakthrough in Prompt Engineering for GPT-4

🚀 Innovative Prompting Method Enhances GPT-4's Capabilities

Microsoft's latest research has unveiled groundbreaking prompting techniques that significantly elevate GPT-4's performance, rivaling specialized AI models in specific fields. This study demonstrates that GPT-4, using advanced prompting methods, can outperform Google’s Med-PaLM 2, which is explicitly trained for medical tasks.

🔧 Advanced Prompting Techniques: A Game-Changer

The research confirms the effectiveness of advanced prompt engineering, a technique already leveraged by expert AI users for generating exceptional image and text outputs. One key method, Chain of Thought (CoT) reasoning, initially developed by Google, enables AI to break down complex tasks into manageable steps, enhancing problem-solving capabilities.

💡 Chain of Thought Prompting: The Core Technique

CoT prompting outlines the necessary steps an AI must take to achieve the desired output. This approach, termed 'Medprompt' in the study, combines CoT reasoning with other techniques to achieve remarkable quality in outputs.

🏆 Medprompt's Superior Performance

In a comparative test against models like Flan-PaLM 540B, Med-PaLM 2, and GPT-4, Medprompt showcased its superiority across four medical benchmark datasets, including MedQA, PubMedQA, MedMCQA, and MMLU.

🌟 Why Medprompt Matters

This research highlights the potential of using a general foundation model like GPT-4, equipped with advanced prompting techniques, to surpass specialized models in various knowledge domains. Medprompt exemplifies how high-quality AI outputs can be achieved without extensive domain-specific training.

📈 Three Key Prompting Strategies

• ⚙️ Dynamic Few-Shot Selection: This method allows AI models to choose relevant training examples, focusing on a smaller yet more representative set of examples.

• 🧠 Self-Generated Chain of Thought: This technique automates the creation of reasoning steps, enhancing the model's problem-solving ability.

• 🔀 Choice Shuffle Ensembling: Addressing position bias and greedy decoding, this approach increases response diversity and reduces bias in multiple-choice question answering.

🌍 Cross-Domain Applicability

Significantly, Medprompt’s techniques are not limited to the medical field but can be applied across various knowledge domains, demonstrating the versatility and broad applicability of this advanced prompting method.

💥 Impact on the Future of Generative AI

Medprompt represents a leap forward in generative AI, offering enhanced capabilities with less effort and training. Its implications for the future of AI and the evolving skill of prompt engineering are profound, marking a new era in AI versatility and adaptability.

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PERPLIXITY AI

🌐 Perplexity Unveils Online Large Language Models with Real-Time Information Access

Perplexity introduces two new online large language models (LLMs) - pplx-7b-online and pplx-70b-online. These models stand out for their ability to access real-time internet data, addressing common limitations in existing LLMs, such as outdated information and factual inaccuracies.

💡 What Is An Online LLM?

Perplexity’s online LLMs uniquely harness the latest internet information, enabling them to provide current answers to queries about recent events or data. This is a significant leap from offline LLMs like GPT-3.5, which rely on pre-existing training data. Perplexity has enhanced these models for accuracy and factualness by fine-tuning them on diverse, quality datasets.

🔍 Comparing PPLX Models to GPT 3.5

Initial tests show that Perplexity’s online LLMs either match or surpass other leading LLMs, including GPT-3.5, in robustness, helpfulness, and knowledge across various subjects. The key advantage is their capability to provide timely, relevant information.

📊 Overcoming Accuracy Challenges in Online LLMs

A research paper titled “FRESHLLMS: Refreshing Large Language Models with Search Engine Augmentation” highlights the struggle of traditional LLMs to provide current information and their tendency to produce inaccurate responses, termed hallucinations. Perplexity’s models address these issues by incorporating real-time internet data, thus maintaining freshness and accuracy in their responses.

🌍 Accessing Perplexity’s New Online LLMs

These models are available to the public through Perplexity’s API and Labs web interface, making them easily accessible for developers and businesses.

📝 Introduction to Perplexity’s LLMs

Perplexity’s new models, pplx-7b-online and pplx-70b-online, are designed to deliver helpful, up-to-date, and factual responses. They leverage internet knowledge to answer time-sensitive queries, a significant upgrade from traditional LLMs.

📈 Evaluating Perplexity’s Online LLMs

Perplexity is committed to building a trusted answer engine. To benchmark their LLMs, they curated evaluation datasets to assess the models' helpfulness, factuality, and freshness. These evaluations involved comparing responses from different models to determine which one best met the criteria.

🔬 Curating the Evaluation Set

The evaluation sets contained 50 diverse prompts each, designed to effectively evaluate the models' performance in helpfulness, factuality, and up-to-dateness. These sets included a range of realistic and challenging answer engine prompts.

🧪 Generating Model Responses

Four models were evaluated:

• pplx-7b-online and pplx-70b-online (Perplexity’s models with internet access).

• gpt-3.5-turbo-1106 (OpenAI’s model).

• llama2-70b-chat (Meta AI’s model).

📋 Ranking Model Responses With Human Evaluation

Human evaluators compared model responses, selecting the ones they preferred holistically and based on the specific evaluation criteria. Internet searches were allowed to verify accuracy.

🏆 Evaluation Results

The evaluation showed that Perplexity's models often outperform gpt-3.5 and llama2-70b in freshness, factuality, and overall quality. Elo scores and pairwise win rates from the evaluation further underscored the superiority of Perplexity’s models in providing accurate and current responses.

💼 Accessing Perplexity’s Online Models

Perplexity announces the public release of its pplx-api, providing access to their online LLMs. This release includes a new usage-based pricing structure and invites users to explore these advanced models.

Finally, Perplexity's introduction of online LLMs marks a pivotal moment in AI democratization. By providing access to the latest web information, these models offer an edge in search and information discovery, pointing towards a future of conversational interfaces with AI assistants that provide timely, accurate, and nuanced responses.

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HOT NEWS

🔍 US Forces Saudi Fund to Withdraw from Altman-Backed AI Chip Startup, Reports Bloomberg

The U.S. government, prioritizing national security, mandated a Saudi Aramco-backed firm to sell its stake in Rain Neuromorphic, an AI chip startup co-founded by OpenAI's Sam Altman. This action, following a CFIUS review, aligns with recent U.S. policies restricting AI tech exports to certain Middle Eastern countries, highlighting the U.S.'s cautious approach towards foreign investments in critical technologies.

🌐 Microsoft Pledges $3.2 Billion for UK AI Expansion

Microsoft's unprecedented 2.5 billion pound investment in the UK, highlighted by PM Sunak, aims to expand AI capabilities by doubling data centers. Despite initial antitrust concerns, Microsoft's renewed commitment, following their Activision Blizzard acquisition, includes 20,000 advanced GPUs and a training scheme, enhancing Britain's AI infrastructure and skillset.

📱 Google Android Update Adds AI Image Descriptions, Voice Message Animations

Google's latest update introduces AI-powered features like Voice Moods in Messages for Android, allowing expressive voice messages with animated emojis. TalkBack, an AI-generated image description tool, enhances accessibility for low-vision users. These updates, part of a broader enhancement to Android, Wear OS, and Google TV, start rolling out today, emphasizing Google's commitment to AI-driven user experience improvements.

🛍️ Mastercard Unveils Shopping Muse AI for Ideal Gift Selections

Mastercard has unveiled Shopping Muse, utilizing Dynamic Yield, a personalization technology platform to provide AI-driven, personalized gift recommendations via chatbot interactions, utilizing consumer data and purchase history, enhancing retail efficiency and customer satisfaction while addressing key ethical issues like privacy and bias. Notably, Shopping Muse is a further step in Dynamic Yield's ongoing journey into consumer-centric AI applications.

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