task_categories:
- text-ranking
tags:
- creative-writing
- llm-evaluation
- preference-alignment
- reward-modeling
- benchmark
- reddit
dataset_info:
features:
- name: prompt
dtype: string
- name: chosen_story
dtype: string
- name: rejected_story
dtype: string
- name: chosen_timestamp
dtype: timestamp[ns]
- name: rejected_timestamp
dtype: timestamp[ns]
- name: chosen_upvotes
dtype: int64
- name: rejected_upvotes
dtype: int64
splits:
- name: train
num_bytes: 276261399
num_examples: 43827
download_size: 172500713
dataset_size: 276261399
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
LitBench is the first standardized benchmark and paired dataset for reliable evaluation of creative writing generated by large language models (LLMs). It addresses the challenge of evaluating open-ended narratives, which lack ground truths. The dataset comprises a held-out test set of 2,480 debiased, human-labeled story comparisons drawn from Reddit and a 43,827-pair training corpus of human preference labels. LitBench facilitates benchmarking zero-shot LLM judges and training reward models for creative writing verification and optimization.
Paper: LitBench: A Benchmark and Dataset for Reliable Evaluation of Creative Writing
Project Page (Hugging Face Collection): https://huggingface.co/collections/SAA-Lab/litbench-68267b5da3aafe58f9e43461
Sample Usage
You can load the dataset using the Hugging Face datasets library:
from datasets import load_dataset
dataset = load_dataset("SAA-Lab/LitBench")
# Access the training split
train_dataset = dataset["train"]
# Print the first example
print(train_dataset[0])
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