Wonder how well it fares for longer instructions — I played around a while ago with using instruction induction to infer “instruction templates” from examples and it seemed to work:
LLMS
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Experimental Optimization of AI Instruction Prompts
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Cool extension of Honovich et al.’s “instruction induction”, experimentally optimizing to find the best instruction prompt for a given set of task examples: https://t.co/h4Rd2A9JwY
— Riley Goodside (@goodside) 4 novembre 2022Cool extension of Honovich et al.’s “instruction induction”, experimentally optimizing to find the best instruction prompt for a given set of task examples:
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Large Language Models Achieve Human-Level Prompt Engineering
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Large Language Models Are Human-Level Prompt Engineers Zhou et al.: https://
arxiv.org/abs/2211.01910 #MachineLearning #DeepLearning #ArtificialIntelligence -
Inverse Scaling Observed Up to 62B Model Parameters
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We also showed inverse scaling up to 62B on our model / prompt setup
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Powerful Tool for K-Shot Datasets and Prompt Chaining with Collapsible Tree UI
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@dust4ai takes some work to wrap your head around, but it's very powerful — gives a collapsible tree UI for representing k-shot example datasets, prompt templates, and prompt chaining with intermediate JS code. Replaces a lot of code around prompt APIs. http://
dust.tt -
Langchain Python Package Implements Advanced LLM Prompting Techniques
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@hwchase17
's Python package Langchain implements and lets you easily compose many published LLM prompting techniques. Implements self-asking, web search, REPL math, and several of my own prompts. -
EveryPrompt Extends Playground for Prompt Variables and API Integration
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@everyprompt extends Playground in a similar way: Putting variables in prompts and giving you a single button to go from prompt to API. Has nice developer-oriented touches in the UI too — e.g. displaying invisible chars as ghosts. https://
everyprompt.com -
Humanloop Playground enhances prompts and API endpoint creation for AI
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First up, @humanloop has a Playground that brings variable interpolation to prompts and lets you turn them into API endpoints. Once you're deployed, it also lets you collect past generations along with user behavioral feedback for fine-tunes. https://
humanloop.com -
Cohere Launches Sandbox Open-Source Packages for LLM Development
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We are excited to announce Sandbox, a set of experimental open-source packages on GitHub that give you the tooling, functions, demos, and libraries you need to get started with large language models (LLMs) fast! Dive in https://
txt.cohere.ai/introducing-sa
ndbox-coheres-experimental-open-source-initiative/
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Cohere Empowers Everyone to Build AI Apps Without NLP Expertise
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At Cohere, we want to empower everyone—from beginners to experts—to build apps, products, services, or even entire companies on top of our language AI platform without having extensive expertise in NLP.