metadata

configs:
  - config_name: query
    data_files:
      - split: full
        path: query/*
  - config_name: corpus_clean
    data_files:
      - split: full
        path: corpus_clean/*
  - config_name: corpus_s2orc
    data_files:
      - split: full
        path: corpus_s2orc/*

LitSearch: A Retrieval Benchmark for Scientific Literature Search

This dataset contains the query set and retrieval corpus for our paper LitSearch: A Retrieval Benchmark for Scientific Literature Search. We introduce LitSearch, a retrieval benchmark comprising 597 realistic literature search queries about recent ML and NLP papers. LitSearch is constructed using a combination of (1) questions generated by GPT-4 based on paragraphs containing inline citations from research papers and (2) questions about recently published papers, manually written by their authors. All LitSearch questions were manually examined or edited by experts to ensure high quality.

This dataset contains three configurations:

query containing 597 queries accomanied by gold paper IDs, specificity and quality annotations, and metadata about the source of the query.
corpus_clean containing 64183 documents. We provide the extracted titles, abstracts and outgoing citation paper IDs.
corpus_s2orc contains the same set of 64183 documents expressed in the Semantic Scholar Open Research Corpus (S2ORC) schema along with all available metadata.

Each configuration has a single 'full' split.

Usage

You can load the configurations as follows:

from datasets import load_dataset

query_data = load_dataset("princeton-nlp/LitSearch", "query", split="full")
corpus_clean_data = load_dataset("princeton-nlp/LitSearch", "corpus_clean", split="full")
corpus_s2orc_data = load_dataset("princeton-nlp/LitSearch", "corpus_s2orc", split="full")