pal
memprompt
| pal | memprompt | |
|---|---|---|
| 4 | 4 | |
| 436 | 320 | |
| 1.4% | - | |
| 3.1 | 1.7 | |
| 10 months ago | about 1 year ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
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pal
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Prompt Engineering Guide: Guides, papers, and resources for prompt engineering
Using the terminology that I'm working with this is an example of a second-order analytic augmentation!
Here's another approach of second-order analytic augmentation, PAL: https://reasonwithpal.com
And third-order, Toolformer: https://arxiv.org/abs/2302.04761
The difference isn't in what is going on but rather with framing the approach within the analytic-synthetic distinction developed by Kant and the analytic philosophers who were influenced by his work. There's a dash of functional programming thrown in for good measure!
I have scribbled on a print-out of the article on my desk:
Nth Order - [R] Faithful Chain-of-Thought Reasoning
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GPT-3: Techniques to improve reliability
GitHub: https://github.com/reasoning-machines/pal
tl;dr -- LLMs are bad at basic arithmetic and logic (as their opening examples with math word problems show), but they do much better if instead of asking them for the answer, you ask for code to compute the answer. Then evaluate or run the code to get the answer.
memprompt
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Allen Institute for Artificial Intelligence Introduces MemPrompt: A New Method to “fix” GPT-3 After Deployment with User Interaction
Quick Read: https://www.marktechpost.com/2022/12/18/allen-institute-for-artificial-intelligence-introduces-memprompt-a-new-method-to-fix-gpt-3-after-deployment-with-user-interaction/ Paper: https://arxiv.org/abs/2201.06009 Code: https://github.com/madaan/memprompt
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Building a Virtual Machine Inside ChatGPT
It's already possible to get some of this effect with codex. The trick is to keep appending the interaction in the prompt (to maintain a memory of sorts).
For examples, you can replicate all the prompts here: https://twitter.com/yoavgo/status/1599200756631887872 with prompt + memory.
The notebook at https://github.com/madaan/memprompt/blob/main/YoavsPythonPro... shows a demo of this.
Some of these ideas were earlier discussed in our work on memory-assisted prompting [1].
[1] https://arxiv.org/pdf/2201.06009.pdf.
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[D] Paper Review Video - Memory-assisted prompt editing to improve GPT-3 after deployment
Code for https://arxiv.org/abs/2201.06009 found: https://github.com/madaan/memprompt
What are some alternatives?
openai-cookbook - Examples and guides for using the OpenAI API
unilm - Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
qagnn - [NAACL 2021] QAGNN: Question Answering using Language Models and Knowledge Graphs 🤖
gpt-scrolls - A collaborative collection of open-source safe GPT-3 prompts that work well
prompt-lib - A set of utilities for running few-shot prompting experiments on large-language models
gpt-neox - An implementation of model parallel autoregressive transformers on GPUs, based on the DeepSpeed library.
empirical-philosophy - A collection of empirical experiments using large language models and other neural network architectures to test the usefulness of metaphysical constructs.
Errbot - Errbot is a chatbot, a daemon that connects to your favorite chat service and bring your tools and some fun into the conversation.
temporal-graph-gen - Pre-trained models for our work on Temporal Graph Generation
googler - :mag: Google from the terminal
Prompt-Engineering-Guide - 🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
assistant