Implications: 1. U-shaped scaling means that inverse scaling may not hold when extrapolated to larger models. 2. The term “inverse scaling task” is underspecified. A task can be inverse scaling for one type of prompting and positive or U-shaped for another type of prompting.
PROMPT ENGINEERING
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CoT Prompting Defends Against Inverse Scaling in Math Tasks
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Second, we show that CoT prompting can defend against inverse scaling. For instance, CoT prompting achieves 100% on 7 out of 8 subtasks for Redefine Math.
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Exploring Instruction Induction for Inferring AI Instruction Templates
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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:
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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 -
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