YichenZW / Pacing

This repository includes the code implementation of the paper Improving Pacing in Long-Form Story Planning by Yichen Wang, Kevin Yang, Xiaoming Liu, and Dan Klein.

Improving Pacing in Long-Form Story Planning

This repository includes the code implementation of the paper Improving Pacing in Long-Form Story Planning by Yichen Wang, Kevin Yang, Xiaoming Liu, and Dan Klein. Corresponding datasets are in the huggingface hub.

Installation

(1) Install Python 3.8.13 (Recommended). Other similar versions should also work fine.

(2) Clone the repo and move into it.

git clone https://github.com/YichenZW/Pacing
cd Pacing

(3) Install the required packages. You are recommended to do it in a Conda environment.

pip install -r requirements.txt

(4) Install the repo by running

pip install -e .

(5) Set your OpenAI API key (or sign up and generate one on the OpenAI website)

echo "export OPENAI_API_KEY='yourkey'" >> ~/.zshrc
source ~/.zshrc

or simply add the following lines at the beginning of ChatGPT_API.py and remove all the openai.api_key = os.environ['OPENAI_API_KEY'] lines.

import os
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]

(6) Download the GPT_BookSum dataset from the huggingface hub. Save it into data/.

(7) Train and save the concreteness evaluator.

python concrete_evaluator/train.py

Note: For fair and quicker evaluation, it is recommended to run the save_for_val() and save_for_test() in the dataset.py first.

python dataset.py

(8) (Optional) Download the WritingPrompts dataset if you want. Save it in the data/WritingPrompts/. You can also build the prompt dataset or input individual prompts by yourself.

(9) Run the outline generator.

python run_outline.py

Evaluation

gpt4_eval/gpt4_eval_single.py is for evaluating the quality of outlines and story excerpts by gpt-4. For the detailed prompt settings for each attribute, please follow the appendix. concrete_evaluator/val.py is for evaluating the performance of the concreteness evaluator. For other human evaluation settings and interfaces, please refer to the appendix of the paper.

Downstream Story Generation

We mainly follow the DOC generation framework to generate complete stories. The full description and instruction for DOC is at [this page](GitHub - yangkevin2/doc-story-generation. Also, a copy at story_generation/README.md. We also utilize a simplified version of DOC to generate a fixed-length story passage for each outline item rather than dynamically varying the passage length as the vanilla DOC. Furthermore, we modify DOC to use ChatGPT rather than their original OPT-175B.

Install the environment first via pip install -r story_generation/requirements.txt. Then install this repo with pip install -e story_generation/..

a) using ChatGPT version DOC:

cd story_generation
CUDA_VISIBLE_DEVICES=0 python -u scripts/main.py {{{ALPA_ARGS}}} --controller longformer_classifier longformer_classifier fudge_controller --loader alignment coherence fine_coherence --controller-load-dir doc_data/ckpt/relevance_reranker doc_data/ckpt/coherence_reranker doc_data/ckpt/detailed_controller --controller-model-string allenai/longformer-base-4096 allenai/longformer-base-4096 facebook/opt-350m --load-outline-file output/plan.pkl --no-editor --include-future-context --control-strength 1 1 0 --control-strength-substep-increment 3 --save-complete-file output/story.pkl --log-file output/story.log

setting --draft-model-string to gpt-3.5-turbo if you what to use ChatGPT instead of original OPT-175B. ALPA_ARGS is only needed if you want to use the free public Alpa OPT-175B API.

For other detailed descriptions, please refer to DOC repo yangkevin2/doc-story-generation, Section Main Story Generation Command.

If you want to generate a batch of stories, you can refer to the scripts story_generation/scripts/gen_baseline.sh and story_generation/scripts/gen_concoct.sh.

b) using the simplified version DOC with ChatGPT:

python story_generation/direct_gen.py

Citations

@article{wang2023improving,
  title={Improving Pacing in Long-Form Story Planning},
  author={Wang, Yichen and Yang, Kevin and Liu, Xiaoming and Klein, Dan},
  journal={arXiv preprint arXiv:2311.04459},
  year={2023}
}

EMNLP2023 citation:

@inproceedings{wang-etal-2023-improving-pacing,
    title = "Improving Pacing in Long-Form Story Planning",
    author = "Wang, Yichen  and
      Yang, Kevin  and
      Liu, Xiaoming  and
      Klein, Dan",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.723",
    doi = "10.18653/v1/2023.findings-emnlp.723",
    pages = "10788--10845",
    abstract = "Existing LLM-based systems for writing long-form stories or story outlines frequently suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details, resulting in a jarring experience for the reader. We propose a **CONC**rete **O**utline **C**on**T**rol (CONCOCT) system to improve pacing when automatically generating story outlines. We first train a *concreteness evaluator* to judge which of two events is more concrete (low-level-detailed). This evaluator can then be used to control pacing in hierarchical outline generation; in this work, we explore a *vaguest-first* expansion procedure that aims for uniform pacing. We further use the evaluator to filter new outline items based on predicted concreteness. Compared to a baseline hierarchical outline generator, humans judge CONCOCT{'}s pacing to be more consistent over 57{\%} of the time across multiple outline lengths; the gains also translate to downstream stories. All code, data, and models are open-sourced.",
}