Learn how to use large language models (LLMs) to generate product descriptions for ads quickly and at scale. Get started ↓ https://
hubs.li/Q01rSBpt0 PS: This social post was generated using text summarization by Cohere.
LLMS
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Using LLMs to Generate Product Descriptions at Scale
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Democratizing Speech Technology at Lightning Speed
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Awww! Thanks, Merve!! Looking forward to democratising speech at lightning speed in the coming months <3
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New Spellbook AI Tool from Scale AI Enhances Prompt Engineering
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Just dropped: Spellbook from @scale_AI. Great ideas in here — couples prompts with uploaded training data to automatically write k-shots, give evaluation metrics for prompt variants; also lets you turn prompts to spreadsheet functions https://t.co/hNGNXmGJtV
— Riley Goodside (@goodside) 9 novembre 2022Just dropped: Spellbook from @scale_AI
. Great ideas in here — couples prompts with uploaded training data to automatically write k-shots, give evaluation metrics for prompt variants; also lets you turn prompts to spreadsheet functions -
Formal Informal Languages: Modern Prompt Engineering Approaches
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Formal Informal Languages — An interesting take on (current) prompt engineering https://
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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://
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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