Skip to content

Proceedings - Lateral Reading 2024

Overview of the TREC 2024 Lateral Reading Track

Dake Zhang, Mark D. Smucker, Charles L. A. Clarke

Abstract

The current web landscape, characterized by abundant information and widespread misinformation, highlights the pressing need for people to evaluate the trustworthiness of online content effectively. However, this remains a daunting challenge for many internet users. The TREC 2024 Lateral Reading Track seeks to address this issue by supporting the use of lateral reading, a proven strategy used by professional fact-checkers, to help users evaluate news articles more effectively and efficiently. In its first year, the track had two tasks: (1) generating questions that readers should consider when assessing the trustworthiness of the given news articles, and (2) retrieving documents to help answer these questions. This paper presents an overview of the track, including its objectives, methodologies, resources, and evaluation results. Our evaluation of the submitted runs shows the significant challenges these tasks pose to existing approaches, including state-of-the-art large language models. Further details on this track can be found on its website: https://trec-dragun.github.io/.

Bibtex 
@inproceedings{coordinators-trec2024-papers-proc-5,
    title = {Overview of the TREC 2024 Lateral Reading Track},
    author = {Dake Zhang and Mark D. Smucker and Charles L. A. Clarke},
    booktitle = {Proceedings of the 33th Text {REtrieval} Conference (TREC 2024)},
    year = {2024},
    address = {Gaithersburg, Maryland},
    series = {NIST SP 1329}
}

Monster Ranking

Charles L. A. Clarke, Siqing Huo, Negar Arabzadeh

Bibtex 
@inproceedings{WaterlooClarke-trec2024-papers-proc-1,
    title = {Monster Ranking},
    author = {Charles L. A. Clarke and Siqing Huo and Negar Arabzadeh},
    booktitle = {Proceedings of the 33th Text {REtrieval} Conference (TREC 2024)},
    year = {2024},
    address = {Gaithersburg, Maryland},
    series = {NIST SP 1329}
}