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. ↓
LLMS
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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 -
Language Models as Realistic Tabular Data Generators
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Language Models are Realistic Tabular Data Generators Borisov et al.: https://
arxiv.org/abs/2210.06280 #ArtificialIntelligence #DeepLearning #MachineLearning -
365-Day Lookback Window Mixed Media Model Strategy
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Sir, you need a 365 / 365 look back window and switch to a mixed media model to make it work
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Prompt Engineering for AI Agents: Step-by-Step Operation Sequencing
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The game is “least to most” — have it write out the most mechanical, reliably performed operations first. Then gradually move up to more interpretive and analytical steps, so it’s never asked to do anything multi-step in its head. The prompt should feel like an income tax form.
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GPT-3 Tokenization Challenges and Model Limitations
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+1 for this approach. GPT-3’s tokenization is a big gotcha. Model doesn’t see text as sequences of characters unless you add spacing/hyphens. It can’t even reliably tell you the last letter of a word without first rewriting it that way. Similar for counting length:
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Large Language Models Demonstrate Self-Improvement Capabilities
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Large Language Models Can Self-Improve Huang et al.: https://
arxiv.org/abs/2210.11610 #ArtificialIntelligence #DeepLearning #MachineLearning -
K-Shot Learning for Non-RLHF Tuned LLMs
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I’ve never seen it work on anything other than RLHF-tuned GPT-3. I’ve wondered if you could k-shot a non-RLHF model with meta-examples of instruction templates followed by input/output pairs for each, but haven’t tried it.