Those with access to DALL-E 3, I would appreciate your notes on attempts replication of this study in The Lancet, using (a) the literal prompts used there, and (b) minor variations thereof. https://
thelancet.com/journals/langl
o/article/PIIS2214-109X(23)00329-7/fulltext
…
GENERATIVE AI
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Replicating Lancet Study Prompts with DALL-E 3
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DALL-E vs Midjourney: Performance Comparison and Results
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Is the success that some are reporting there a function of DALL-E being legit better than Midjourney? Of specific prompts having somehow been addressed? Please report both successes and failures.
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Tools and Methods for Exploring Different AI Models
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What do you currently use for exploring different AI models?
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Salesforce Launches Einstein Copilot AI Assistant for CRM Applications
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Salesforce introduces new AI assistant, Einstein Copilot, for all its CRM apps https://
bit.ly/3Znmoqi #AI #MachineLearning #DeepLearning #LLMs #DataScience -
LLMs Self-Improvement Through Feedback and Real-Time Learning
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All of these methods explore how LLMs can self-improve based on fine-tuning, implicit human preferences and iterative prompting techniques. LLMs, like humans can take constructive feedback and become better. Now if they can only do it in real-time by just listening /16
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GPT-4 Self-Improving Code Through Recursive Scaffolding Programs
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The core idea begins with an initial seed 'improver' scaffolding program that utilizes the language model to improve a solution to some downstream task. They demonstrate that GPT-4 is capable of writing code that can call itself to improve itself. /15
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Self-Taught Optimizer: Language Model for Recursive Solution Enhancement
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Finally, Self-Taught Optimizer (STOP), employs a language model to enhance arbitrary solutions and then applies this recursively to improve itself. /14
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Automated LLM Improvement Through Quality Gap Maximization
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Instead of merely maximizing the response quality for a given input, the focus is shifted to maximizing the quality gap of the response, thus promoting self-improvement. This method takes human preferences and creates an automated way to improve LLMs over time. /10
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PIT Framework: Learning from Human Preferences and RLHF Reformulation
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The PIT framework focuses on learning from human preference data, coupled with its unique reformulation of the RLHF objective. Humans indicate their preferences on LLM outputs and this data is used to train reward models. The RLHF objective is reformulated. /9
