LLMS

→ View original post on X — @baiduresearch

ERNIE-Layout Achieves SOTA Results on Document Understanding Tasks

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@baiduresearch

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04 November 2022 17h00

Introducing ERNIE-Layout, a layout knowledge enhanced document pre-training model that obtained SOTA results on 11 datasets and won 1st place on DocVQA (exceeding 90 for the 1st time) and WebSRC. Check Zero-shot Demo @huggingface
: http://
huggingface.co/spaces/PaddleP
addle/ERNIE-Layout
…

Exploring Instruction Induction for Inferring AI Instruction Templates

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04 November 2022 16h08

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:

Experimental Optimization of AI Instruction Prompts

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04 November 2022 16h08

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 2022

Cool extension of Honovich et al.’s “instruction induction”, experimentally optimizing to find the best instruction prompt for a given set of task examples:

→ View original post on X — @montreal_ai

Large Language Models Achieve Human-Level Prompt Engineering

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@montreal_ai

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04 November 2022 13h57

Large Language Models Are Human-Level Prompt Engineers Zhou et al.: https://
arxiv.org/abs/2211.01910 #MachineLearning #DeepLearning #ArtificialIntelligence

→ View original post on X — @_jasonwei

Inverse Scaling Observed Up to 62B Model Parameters

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@_jasonwei

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04 November 2022 7h58

We also showed inverse scaling up to 62B on our model / prompt setup

Powerful Tool for K-Shot Datasets and Prompt Chaining with Collapsible Tree UI

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03 November 2022 21h12

@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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03 November 2022 20h12

@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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03 November 2022 20h12

@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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03 November 2022 20h12

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