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“GPT-3 Creative Fiction”, Branwen 2020

GPT-3: “GPT-3 Creative Fiction”⁠, Gwern Branwen (2020-06-19; ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ; backlinks; similar):

Creative writing by OpenAI’s GPT-3 model, demonstrating poetry, dialogue, puns, literary parodies, and storytelling. Plus advice on effective GPT-3 prompt programming & avoiding common errors.

I continue my AI poetry generation experiments with OpenAI’s 2020 GPT-3, which is 116× larger, and much more powerful, than the 2019 GPT-2⁠. GPT-3, however, is not merely a quantitative tweak yielding “GPT-2 but better”—it is qualitatively different, exhibiting eerie runtime learning capabilities allowing even the raw model, with zero finetuning, to “meta-learn” many textual tasks purely by example or instruction. One does not train or program GPT-3 in a normal way, but one engages in dialogue and writes prompts to teach GPT-3 what one wants.

Experimenting through the OpenAI Beta API in June 2020, I find that GPT-3 does not just match my finetuned GPT-2-1.5b-poetry for poem-writing quality, but exceeds it, while being versatile in handling poetry⁠, Tom Swifty puns⁠, science fiction, dialogue like Turing’s Turing-test dialogue⁠, literary style parodies… As the pièce de résistance, I recreate Stanislaw Lem’s Cyberiad’s “Trurl’s Electronic Bard” poetry using GPT-3. (Along the way, I document instances of how the BPE text encoding unnecessarily damages GPT-3’s performance on a variety of tasks, how to best elicit the highest-quality responses, common errors people make in using GPT-3, and test out GPT-3’s improvements in NN weak points like logic or commonsense knowledge.)

GPT-3’s samples are not just close to human level: they are creative, witty, deep, meta, and often beautiful. They demonstrate an ability to handle abstractions, like style parodies, I have not seen in GPT-2 at all. Chatting with GPT-3 feels uncannily like chatting with a human. I was impressed by the results reported in the GPT-3 paper, and after spending a week trying it out, I remain impressed.

This page records GPT-3 samples I generated in my explorations, and thoughts on how to use GPT-3 and its remaining weaknesses⁠. I hope you enjoy them even a tenth as much as I enjoyed testing GPT-3 and watching the completions scroll across my screen.

“GPT-2 Preference Learning for Music Generation”, Branwen 2019

GPT-2-preference-learning: “GPT-2 Preference Learning for Music Generation”⁠, Gwern Branwen (2019-12-16; ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ; backlinks; similar):

Experiments with OpenAI’s ‘preference learning’ approach, which trains a NN to predict global quality of datapoints, and then uses reinforcement learning to optimize that directly, rather than proxies. I am unable to improve quality, perhaps due to too-few ratings.

Standard language generation neural network models, like GPT-2⁠, are trained via likelihood training to imitate human text corpuses. Generated text suffers from persistent flaws like repetition, due to myopic generation word-by-word, and cannot improve on the training data because they are trained to predict ‘realistic’ completions of the training data.

A proposed alternative is to use reinforcement learning to train the NNs, to encourage global properties like coherence & lack of repetition, and potentially improve over the original corpus’s average quality. Preference learning trains a reward function on human ratings, and uses that as the ‘environment’ for a blackbox DRL algorithm like PPO⁠.

OpenAI released a codebase implementing this dual-model preference learning approach for textual generation, based on GPT-2. Having previously used GPT-2 for poetry & music generation⁠, I experimented with GPT-2 preference learning for unconditional music and poetry generation.

I found that preference learning seemed to work better for music than poetry, and seemed to reduce the presence of repetition artifacts, but the results, at n ≈ 7,400 ratings compiled over 23 iterations of training+sampling November 2019–January 2020, are not dramatically better than alternative improvements like scaling up models or more thorough data-cleaning or more stringent sample curation. My blind ratings using n ≈ 200 comparisons showed no large advantage for the RL-tuned samples (winning only 93 of 210 comparisons, or 46%).

This may be due to insufficient ratings, bad hyperparameters, or not using samples generated with common prefixes, but I suspect it’s the former, as some NLP tasks in Ziegler et al 2019 required up to 60k ratings for good performance, and the reward model appeared to achieve poor performance & succumb to adversarial examples easily.

Working with it, I suspect that preference learning is unnecessarily sample-inefficient & data-inefficient, and that the blackbox reinforcement learning approach is inferior to directly using the reward model to optimize text samples, and propose two major architectural overhauls: have the reward model directly model the implied ranking of every datapoint, and drop the agent model entirely in favor of backprop-powered gradient ascent which optimizes sequences to maximize the reward model’s output⁠.

“Killing Rabbits”, Válek 2019

Killing-Rabbits: “Killing Rabbits”⁠, Miroslav Válek (2019-05-06; ⁠, )

“GPT-2 Neural Network Poetry”, Branwen & Presser 2019

GPT-2: “GPT-2 Neural Network Poetry”⁠, Gwern Branwen, Shawn Presser (2019-03-03; ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ; backlinks; similar):

Demonstration tutorial of retraining OpenAI’s GPT-2 (a text-generating Transformer neural network) on large poetry corpuses to generate high-quality English verse.

In February 2019, following up on my 2015–2016 text-generation experiments with char-RNNs⁠, I experiment with the cutting-edge Transformer NN architecture for language modeling & text generation. Using OpenAI’s GPT-2-117M (117M) model pre-trained on a large Internet corpus and nshepperd’s finetuning code, I retrain GPT-2-117M on a large (117MB) Project Gutenberg poetry corpus. I demonstrate how to train 2 variants: “GPT-2-poetry”, trained on the poems as a continuous stream of text, and “GPT-2-poetry-prefix”, with each line prefixed with the metadata of the PG book it came from. In May 2019, I trained the next-largest GPT-2, GPT-2-345M, similarly, for a further quality boost in generated poems. In October 2019, I retrained GPT-2-117M on a Project Gutenberg corpus with improved formatting, and combined it with a contemporary poem dataset based on Poetry Foundation’s website⁠.

With just a few GPU-days on 1080ti GPUs, GPT-2-117M finetuning can produce high-quality poetry which is more thematically consistent than my char-RNN poems, capable of modeling subtle features like rhyming, and sometimes even a pleasure to read. I list the many possible ways to improve poem generation and further approach human-level poems. For the highest-quality AI poetry to date, see my followup pages, “GPT-3 Creative Writing”⁠/​“GPT-3 Non-Fiction”⁠.

For anime plot summaries, see TWDNE⁠; for generating ABC-formatted folk music, see “GPT-2 Folk Music” & “GPT-2 Preference Learning for Music and Poetry Generation”⁠; for playing chess, see “A Very Unlikely Chess Game”⁠; for the Reddit comment generator, see SubSimulatorGPT-2⁠; for fanfiction, the Ao3⁠; and for video games, the walkthrough model⁠. For OpenAI’s GPT-3 followup, see “GPT-3: Language Models are Few-Shot Learners”⁠.

“Vectors 3.0: Even More Aphorisms and Ten-Second Essays”, Richardson 2018

2010-richardson-bythenumbers-vectors30: “Vectors 3.0: Even More Aphorisms and Ten-Second Essays”⁠, James Richardson (2018-10-27; ⁠, ; backlinks; similar):

170 aphorisms, mini essays or poems on life by James Richardson

James Richardson is an American academic poet & critic at Princeton University. Several of his poetry collections feature compilations, typically named “Vectors”, of short nonfiction prose: aphorisms, comments, & “ten-second essays”, reflecting on life. They are among the most popular of his writings.

Vectors 3.0 is excerpted here.

“RNN Metadata for Mimicking Author Style”, Branwen 2015

RNN-metadata: “RNN Metadata for Mimicking Author Style”⁠, Gwern Branwen (2015-09-12; ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ⁠, ; backlinks; similar):

Teaching a text-generating char-RNN to automatically imitate many different authors by labeling the input text by author; additional experiments include imitating Geocities and retraining GPT-2 on a large Project Gutenberg poetry corpus.

Char-RNNs are unsupervised generative models which learn to mimic text sequences. I suggest extending char-RNNs with inline metadata such as genre or author prefixed to each line of input, allowing for better & more efficient metadata, and more controllable sampling of generated output by feeding in desired metadata. A 2015 experiment using torch-rnn on a set of ~30 Project Gutenberg e-books (1 per author) to train a large char-RNN shows that a char-RNN can learn to remember metadata such as authors, learn associated prose styles, and often generate text visibly similar to that of a specified author.

I further try & fail to train a char-RNN on Geocities HTML for unclear reasons.

More successfully, I experiment in 2019 with a recently-developed alternative to char-RNNs⁠, the Transformer NN architecture, by finetuning training OpenAI’s GPT-2-117M Transformer model on a much larger (117MB) Project Gutenberg poetry corpus using both unlabeled lines & lines with inline metadata (the source book). The generated poetry is much better. And GPT-3 is better still.

“Cultural Drift: Cleaning Methods”, Branwen 2013

Sand: “Cultural drift: cleaning methods”⁠, Gwern Branwen (2013-05-07; ⁠, ⁠, ⁠, ⁠, ; backlinks; similar):

Forgotten chores and their use by Romanticism

Some old books mention sandy floors and sprinkling water on the ground; these asides seem to go unnoticed by most/​all readers. I highlight them, explain and discuss their use as now-obsolete cleaning practices, poll Internet users to see how forgotten they are, and ponder implications. In an appendix, I discuss a similar issue I encountered in pre-Space-Race American science fiction.

Miscellaneous