Formal Informal Languages — An interesting take on (current) prompt engineering https://
flip.it/lmLQod
LLMS
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Formal Informal Languages: Modern Prompt Engineering Approaches
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ReAct: Synergizing Reasoning and Acting in Language Models
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Learn how ReAct, a general paradigm for synergizing reasoning and acting in language models, presents more interpretable, diagnosable, and controllable task-solving trajectories while outperforming reasoning and acting only paradigms. Read the blog → https://
goo.gle/3TiNcDp -
ROME: Efficient Factual Editing in GPT Models
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MIT, Northeastern & Technion Propose ROME for Efficient Locating and Editing of Factual Associations in GPT Models
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Non-Experts Answer Expert Questions on MMLU and QuALITY
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We ask non-experts to answer expert-level questions on MMLU, and also ask people to answer questions about long QuALITY passages under a time limit that’s too short for a careful read.
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Scalable Oversight Framework and Language Model Question-Answering Proof of Concept
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Along with developing a framework for scalable oversight, we also conduct a proof of concept experiment that demonstrates a couple of question-answering tasks that work well under this paradigm with current language models:
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AI Systems Improving Human Oversight of Large Language Models
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In "Measuring Progress on Scalable Oversight for Large Language Models” we show how humans could use AI systems to better oversee other AI systems, and demonstrate some proof-of-concept results where a language model improves human performance at a task.
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LangChain 0.0.9: Hugging Face Embeddings and API Key Management
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LangChain version 0.0.9 Support for embeddings with @huggingface through `sentence_transformers` from @abdrahman_issam (example notebook: https://colab.research.google.com/drive/1lbjO0-nITa5c8RXfagsIZDqxZ_mVl_2k?usp=sharing…) Better support for different ways of specifying API keys from @camjuu
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How Much Does Attention Actually Attend in Transformers?
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How Much Does Attention Actually Attend? Questioning the Importance of Attention in Pretrained Transformers Hassid et al.: https://
arxiv.org/abs/2211.03495 #ArtificialIntelligence #DeepLearning #MachineLearning -
Smaller Models with Better Data Can Outperform Larger Ones
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Great point. We are seeing more and more than smaller models with better objectives or data can beat big ones! My main point is that an approach shouldnt go away as models get better. Scale is just one way of getting better
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Large Language Models Insufficient for Pharma and Finance
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Large language models like #GPT3 aren’t good enough for pharma and finance https://
thenextweb.com/news/large-lan
guage-models-like-gpt-3-arent-good-enough-for-pharma-finance
… @thenextweb #AI #MachineLearning #BigData #Analytics #Robots #DeepLearning #100DaysofCode #IoT #serverless #DEVCommunity #womenwhocode #DigitalTransformation #Python #DataScience