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:
LLMS
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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 -
Unleashing LLM Potential: Cohere and Scale AI Leaders Discuss
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Check out this fireside chat with Cohere Co-founder and CEO @aidangomezzz and @scale_AI Founder and CEO @alexandr_wang about how to unleash the full potential of large language models. ↓
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NLLB-200 Achieves Superior Translation Quality Across All Languages
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Across all languages, NLLB-200 is seeing the best results for translations modified <10% compared to all other MT services on the platform — a strong signal for the quality of translations that are being generated. 4/5
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Recommendation to Follow Le Hou’s Research on Large Language Models
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People interested in large LMs should follow Le Hou (
@Hou_Le
) at @GoogleAI
, who made a new twitter account recently Le has done great work such as Flan, and self-play for reasoning (
https://
arxiv.org/abs/2210.11610). I'm sure we'll see more great work from him 🙂 -
Cohere NLP JumpStart Webinar with TechCrunch and Google Cloud
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Cohere is helping developers and startups build meaningful apps with language AI. Join us for an #NLPJumpStart session with Kemi Tijani, who will host the @TechCrunch webinar available on November 10, 2022, in partnership with @gcloudpartners
. → https://
hubs.li/Q01rFN3w0