- See Also
- Gwern
-
Links
- “Controlled Text Generation via Language Model Arithmetic”, Dekoninck et al 2023
- “DiffusER: Discrete Diffusion via Edit-Based Reconstruction”, Reid et al 2022
- “Prompt-And-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer With Small Language Models”, Suzgun et al 2022
- “Using Natural Language Prompts for Machine Translation”, Garcia & Firat 2022
- “A Review of Text Style Transfer Using Deep Learning”, Toshevska & Gievska 2021
- “A Recipe For Arbitrary Text Style Transfer With Large Language Models”, Reif et al 2021
- “Story Centaur: Large Language Model Few Shot Learning As a Creative Writing Tool”, Swanson 2021
- “TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling”, Riley et al 2020
- “Improving GAN Training With Probability Ratio Clipping and Sample Reweighting”, Wu et al 2020
- “CTRL: A Conditional Transformer Language Model For Controllable Generation”, Keskar et al 2019
- “Multi-Content GAN for Few-Shot Font Style Transfer”, Azadi et al 2017
- “Style Transfer in Text: Exploration and Evaluation”, Fu et al 2017
- “Evaluating Prose Style Transfer With the Bible”, Carlson et al 2017
- “DeepTingle”, Khalifa et al 2017
- “A Neural Network System for Transformation of Regional Cuisine Style”, Kazama et al 2017
- “A Neural Algorithm of Artistic Style”, Gatys et al 2015
- “Introducing AI Dungeon Translate: AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button. [ 🤔 💯 🤷♂️ 🤔 🤔 🤔 💯]”
- “OpenAI API Alchemy: Emoji Storytelling 🤖”
- “AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button.”
- “This Is the OpenAI API. It Makes Spookily Good Twitter Bots. 13⁄10 Would Retweet”
- Sort By Magic
- Miscellaneous
- Link Bibliography
See Also
Gwern
“GPT-3 Creative Fiction”, Gwern 2020
Links
“Controlled Text Generation via Language Model Arithmetic”, Dekoninck et al 2023
“DiffusER: Discrete Diffusion via Edit-Based Reconstruction”, Reid et al 2022
“Prompt-And-Rerank: A Method for Zero-Shot and Few-Shot Arbitrary Textual Style Transfer With Small Language Models”, Suzgun et al 2022
“Using Natural Language Prompts for Machine Translation”, Garcia & Firat 2022
“A Review of Text Style Transfer Using Deep Learning”, Toshevska & Gievska 2021
“A Recipe For Arbitrary Text Style Transfer With Large Language Models”, Reif et al 2021
A Recipe For Arbitrary Text Style Transfer with Large Language Models
“Story Centaur: Large Language Model Few Shot Learning As a Creative Writing Tool”, Swanson 2021
Story Centaur: Large Language Model Few Shot Learning as a Creative Writing Tool:
“TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling”, Riley et al 2020
TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling
“Improving GAN Training With Probability Ratio Clipping and Sample Reweighting”, Wu et al 2020
Improving GAN Training with Probability Ratio Clipping and Sample Reweighting
“CTRL: A Conditional Transformer Language Model For Controllable Generation”, Keskar et al 2019
CTRL: A Conditional Transformer Language Model For Controllable Generation
“Multi-Content GAN for Few-Shot Font Style Transfer”, Azadi et al 2017
“Style Transfer in Text: Exploration and Evaluation”, Fu et al 2017
“Evaluating Prose Style Transfer With the Bible”, Carlson et al 2017
“DeepTingle”, Khalifa et al 2017
“A Neural Network System for Transformation of Regional Cuisine Style”, Kazama et al 2017
A neural network system for transformation of regional cuisine style
“A Neural Algorithm of Artistic Style”, Gatys et al 2015
“Introducing AI Dungeon Translate: AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button. [ 🤔 💯 🤷♂️ 🤔 🤔 🤔 💯]”
“OpenAI API Alchemy: Emoji Storytelling 🤖”
“AI Dungeon Players Can Now Translate Their Stories into Emojis by Just Clicking a Button.”
AI Dungeon players can now translate their stories into emojis by just clicking a button.
“This Is the OpenAI API. It Makes Spookily Good Twitter Bots. 13⁄10 Would Retweet”
This is the OpenAI API. It makes spookily good twitter bots. 13⁄10 would retweet
Sort By Magic
Annotations sorted by machine learning into inferred 'tags'. This provides an alternative way to browse: instead of by date order, one can browse in topic order. The 'sorted' list has been automatically clustered into multiple sections & auto-labeled for easier browsing.
Beginning with the newest annotation, it uses the embedding of each annotation to attempt to create a list of nearest-neighbor annotations, creating a progression of topics. For more details, see the link.
stylometry
style-transfer
text-generation
Miscellaneous
-
/doc/ai/text-style-transfer/2020-brown-gpt3-figure12-largermodelsmetalearn.png: -
https://barryzhang.substack.com/p/our-humble-attempt-at-fine-tuning -
https://chat.openai.com/share/261034ff-f5d5-404c-b354-c9d58e3af509 -
https://twitter.com/DavidSHershey/status/1590027002580721664: -
https://twitter.com/DiffusionPics/status/1600985268273692672: -
https://twitter.com/OwainEvans_UK/status/1636580251676585986 -
https://twitter.com/OwainEvans_UK/status/1636581594642403328 -
https://twitter.com/OwainEvans_UK/status/1636605571637055488 -
https://twitter.com/OwainEvans_UK/status/1636762386085605376 -
https://twitter.com/peligrietzer/status/1598356385623851011: -
https://wordcraft-writers-workshop.appspot.com/stories/allison-parrish -
https://www.newyorker.com/news/our-columnists/whats-the-point-of-reading-writing-by-humans -
https://www.reddit.com/r/ChatGPT/comments/15et6f2/well_i_got_what_i_asked_for/ -
https://www.reddit.com/r/GPT3/comments/rzhrrt/gpt3_being_a_savage_partly_extremely_black_humor/ -
https://www.reddit.com/r/GPT3/comments/ukbba5/the_rickrollian_language_of_william_shakespeare/ -
https://www.reddit.com/r/OpenAI/comments/zcd9yq/making_it_explain_stuff_in_different_styles_is/
Link Bibliography
-
https://arxiv.org/abs/2202.11822#google: “Using Natural Language Prompts for Machine Translation”, Xavier Garcia, Orhan Firat -
https://arxiv.org/abs/1909.05858#salesforce: “CTRL: A Conditional Transformer Language Model For Controllable Generation”, Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, Richard Socher (Salesforce)