Uploaded October 2023 | Updated September 2026, 2 weeks ago
#ai #promptengineering #evolution
Promptbreeder is a self-improving self-referential system for automated prompt engineering. Give it a task description and a dataset, and it will automatically come up with appropriate prompts for the task. This is achieved by an evolutionary algorithm where not only the prompts, but also the mutation-prompts are improved over time in a population-based, diversity-focused approach.
OUTLINE:
0:00 - Introduction
2:10 - From manual to automated prompt engineering
10:40 - How does Promptbreeder work?
21:30 - Mutation operators
36:00 - Experimental Results
38:05 - A walk through the appendix
Paper: arxiv.org/abs/2309.16797
Abstract:
Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification.
Authors: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel
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#ai #promptengineering #evolution
Promptbreeder is a self-improving self-referential system for automated prompt engineering. Give it a task description and a dataset, and it will automatically come up with appropriate prompts for the task. This is achieved by an evolutionary algorithm where not only the prompts, but also the mutation-prompts are improved over time in a population-based, diversity-focused approach.
OUTLINE:
0:00 - Introduction
2:10 - From manual to automated prompt engineering
10:40 - How does Promptbreeder work?
21:30 - Mutation operators
36:00 - Experimental Results
38:05 - A walk through the appendix
Paper: arxiv.org/abs/2309.16797
Abstract:
Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper, we present Promptbreeder, a general-purpose self-referential self-improvement mechanism that evolves and adapts prompts for a given domain. Driven by an LLM, Promptbreeder mutates a population of task-prompts, and subsequently evaluates them for fitness on a training set. Crucially, the mutation of these task-prompts is governed by mutation-prompts that the LLM generates and improves throughout evolution in a self-referential way. That is, Promptbreeder is not just improving task-prompts, but it is also improving the mutationprompts that improve these task-prompts. Promptbreeder outperforms state-of-the-art prompt strategies such as Chain-of-Thought and Plan-and-Solve Prompting on commonly used arithmetic and commonsense reasoning benchmarks. Furthermore, Promptbreeder is able to evolve intricate task-prompts for the challenging problem of hate speech classification.
Authors: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel
Links:
Homepage: ykilcher.com
Merch: ykilcher.com/merch
YouTube: youtube.com/c/yannickilcher
Twitter: twitter.com/ykilcher
Discord: ykilcher.com/discord
LinkedIn: linkedin.com/in/ykilcher
If you want to support me, the best thing to do is to share out the content :)
If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
SubscribeStar: subscribestar.com/yannickilcher
Patreon: patreon.com/yannickilcher
Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq
Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2
Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m
Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n



![[ML News] GPT-3 learns to edit | Google Pathways | Make-A-Scene | CLIP meets GamePhysics | DouBlind
#mlnews #gpt3 #pathways
Your updates on the latest and greatest from the depths of Machine Learning!
Sponsor: Weights & Biases
https://wandb.me/yannic
OUTLINE:
0:00 - Intro
0:15 - Weights & Biases Report about Reports
2:45 - GPT-3 learns to edit
6:30 - Make-A-Scene: Text-to-Image with Human Priors
8:00 - Pathways: Googles new High-Performance ML scheduler
10:45 - DouBlind: Open Peer-Review
12:45 - CLIP meets GamePhysics
14:40 - Residual Quantization pushes Image Generation SOTA
16:15 - Helpful Things
References:
Weights & Biases Report about Reports
https://wandb.ai/wandb/wandb_example/reports/How-many-discoveries-were-lost-because-they-weren-t-written-down VmlldzoxMjY3MDk5
GPT-3 learns to edit
https://openai.com/blog/gpt-3-edit-insert/?utm_source=pocket_mylist
https://beta.openai.com/playground?model=code-davinci-002
Make-A-Scene: Text-to-Image with Human Priors
https://arxiv.org/pdf/2203.13131.pdf
https://www.youtube.com/watch?v=QLTyqoJJKTo
Pathways: Googles new High-Performance ML scheduler
https://arxiv.org/pdf/2203.12533.pdf
DouBlind: Open Peer-Review
https://doublind.com/#web-intro
https://doublind.com/search?query=kilcher
CLIP meets GamePhysics
https://arxiv.org/pdf/2203.11096.pdf
https://www.reddit.com/r/GamePhysics/comments/9rqabp/red_dead_redemption_2_things_you_find_in_rdr2/
https://asgaardlab.github.io/CLIPxGamePhysics/
Residual Quantization pushes Image Generation SOTA
https://arxiv.org/pdf/2203.01941.pdf
https://github.com/kakaobrain/rq-vae-transformer
Helpful Things
https://github.com/TDAmeritrade/stumpy
https://github.com/linkedin/fasttreeshap
https://github.com/vopani/jaxton
https://twitter.com/mark_riedl/status/1507351959422087173?utm_source=pocket_mylist
https://github.com/eilab-gt/NovGrid
https://developer.nvidia.com/isaac-gym
https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
Links:
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BiliBili: https://space.bilibili.com/2017636191
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If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this):
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![[ML News] Stable Diffusion Takes Over! (Open Source AI Art)
#stablediffusion #aiart #mlnews
Stable Diffusion has been released and is riding a wave of creativity and collaboration. But not everyone is happy about this...
Sponsor: NVIDIA
GPU Raffle: https://ykilcher.com/gtc
OUTLINE:
0:00 - Introduction
0:30 - What is Stable Diffusion?
2:25 - Open-Source Contributions and Creations
7:55 - Textual Inversion
9:30 - OpenAI vs Open AI
14:20 - Journalists be outraged
16:20 - AI Ethics be even more outraged
19:45 - Do we need a new social contract?
21:30 - More applications
22:55 - Helpful Things
23:45 - Sponsor: NVIDIA (& how to enter the GPU raffle)
References: https://early-hair-c20.notion.site/Stable-Diffusion-Takes-Over-Referenes-7a2f45b8f7e04ae0ba19dbfcd2b7f7c0
Links:
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![[ML News] This AI completes Wikipedia! Meta AI Sphere | Google Minerva | GPT-3 writes a paper
#mlnews #ai #minerva
This episode is all about models that reason.
OUTLINE:
0:00 - Intro
0:35 - Meta AI learns Wikipedia citations
5:25 - Googles Minerva solves math problems by reading papers
9:10 - GPT-3 writes a paper on itself
13:35 - Jürgen Schmidhuber prompts LeCun for missing citations
References:
Meta AI learns Wikipedia citations
https://tech.fb.com/artificial-intelligence/2022/07/how-ai-could-help-make-wikipedia-entries-more-accurate/
https://ai.facebook.com/blog/introducing-sphere-meta-ais-web-scale-corpus-for-better-knowledge-intensive-nlp/?d=%7B%22u%22%3A100051861999022%2C%22f%22%3A207799259245384%2C%22t%22%3A1658664021%2C%22ed%22%3A[]%7D&s=AWVELTip1y4HowJprXc
https://github.com/facebookresearch/sphere
https://github.com/facebookresearch/side
https://verifier.sideeditor.com/main
https://openreview.net/forum?id=qfTqRtkDbWZ
Googles Minerva solves math problems by reading papers
https://minerva-demo.github.io/#category=Precalculus&index=9
https://ai.googleblog.com/2022/06/minerva-solving-quantitative-reasoning.html
GPT-3 writes a paper on itself
https://www.scientificamerican.com/article/we-asked-gpt-3-to-write-an-academic-paper-about-itself-then-we-tried-to-get-it-published/
https://hal.archives-ouvertes.fr/hal-03701250v1
https://hal.archives-ouvertes.fr/hal-03701250/document
Jürgen Schmidhuber prompts LeCun for missing citations
https://people.idsia.ch/~juergen/lecun-rehash-1990-2022.html
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