Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The spreading of misinformation online
20161.3k citationsMichela Del Vicario, Alessandro Bessi et al.Proceedings of the National Academy of Sciencesprofile →
Language Models as Knowledge Bases?
20191.0k citationsFabio Petroni, Tim Rocktäschel et al.profile →
Lost in the Middle: How Language Models Use Long Contexts
2024285 citationsNelson F. Liu, Kevin Lin et al.Transactions of the Association for Computational Linguisticsprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Fabio Petroni's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Fabio Petroni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Fabio Petroni more than expected).
This network shows the impact of papers produced by Fabio Petroni. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Fabio Petroni. The network helps show where Fabio Petroni may publish in the future.
Co-authorship network of co-authors of Fabio Petroni
This figure shows the co-authorship network connecting the top 25 collaborators of Fabio Petroni.
A scholar is included among the top collaborators of Fabio Petroni based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Fabio Petroni. Fabio Petroni is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Liu, Nelson F., Kevin Lin, John Hewitt, et al.. (2024). Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics. 12. 157–173.285 indexed citations breakdown →
Josifoski, Martin, Nicola De Cao, Maxime Peyrard, Fabio Petroni, & Robert West. (2022). GenIE: Generative Information Extraction. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4626–4643.25 indexed citations
6.
Lewis, Patrick, Barlas Oğuz, Wenhan Xiong, et al.. (2022). Boosted Dense Retriever. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 3102–3117.6 indexed citations
Petroni, Fabio, Patrick Lewis, Aleksandra Piktus, et al.. (2020). How Context Affects Language Models' Factual Predictions. UCL Discovery (University College London).16 indexed citations
10.
Lewis, Patrick, Ethan Perez, Aleksandra Piktus, et al.. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. UCL Discovery (University College London). 33. 9459–9474.2 indexed citations
11.
Fan, Angela, Aleksandra Piktus, Fabio Petroni, et al.. (2020). Generating Fact Checking Briefs. 7147–7161.21 indexed citations
12.
Petroni, Fabio, Tim Rocktäschel, Sebastian Riedel, et al.. (2019). Language Models as Knowledge Bases?. 2463–2473.1022 indexed citations breakdown →
Vicario, Michela Del, Alessandro Bessi, Fabiana Zollo, et al.. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences. 113(3). 554–559.1294 indexed citations breakdown →
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.