Thibault Formal

608 total citations
9 papers, 200 citations indexed

About

Thibault Formal is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Information Systems. According to data from OpenAlex, Thibault Formal has authored 9 papers receiving a total of 200 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 3 papers in Computer Vision and Pattern Recognition and 1 paper in Information Systems. Recurrent topics in Thibault Formal's work include Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Thibault Formal is often cited by papers focused on Topic Modeling (6 papers), Natural Language Processing Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (4 papers). Thibault Formal collaborates with scholars based in France and Italy. Thibault Formal's co-authors include Stéphane Clinchant, Benjamin Piwowarski, Carlos Lassance, Hervé Déjean, Nicola Ferro, Guglielmo Faggioli, Stefano Marchesin and Shuai Wang and has published in prestigious journals such as ACM Transactions on Information Systems, HAL (Le Centre pour la Communication Scientifique Directe) and Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval.

In The Last Decade

Thibault Formal

9 papers receiving 188 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Thibault Formal France	4	162	80	55	12	11	9	200
Jingtao Zhan China	6	231 1.4×	121 1.5×	74 1.3×	13 1.1×	10 0.9×	14	272
Scott Yih United States	9	272 1.7×	90 1.1×	48 0.9×	13 1.1×	10 0.9×	19	303
Jon Saad-Falcon United States	4	127 0.8×	52 0.7×	35 0.6×	6 0.5×	7 0.6×	9	170
Hidetaka Kamigaito Japan	10	277 1.7×	41 0.5×	29 0.5×	11 0.9×	11 1.0×	77	306
Semih Yavuz United States	12	345 2.1×	44 0.6×	50 0.9×	8 0.7×	7 0.6×	28	365
Hyokun Yun United States	5	92 0.6×	59 0.7×	53 1.0×	22 1.8×	7 0.6×	7	162
Qingqing Cai China	6	198 1.2×	46 0.6×	44 0.8×	33 2.8×	6 0.5×	11	233
Yusuke Oda Japan	7	215 1.3×	49 0.6×	108 2.0×	11 0.9×	26 2.4×	11	267
Iulia Turc United States	4	139 0.9×	35 0.4×	21 0.4×	13 1.1×	9 0.8×	5	158

Countries citing papers authored by Thibault Formal

Since Specialization

Citations

This map shows the geographic impact of Thibault Formal'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 Thibault Formal with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thibault Formal more than expected).

Fields of papers citing papers by Thibault Formal

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thibault Formal. 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 Thibault Formal. The network helps show where Thibault Formal may publish in the future.

Co-authorship network of co-authors of Thibault Formal

This figure shows the co-authorship network connecting the top 25 collaborators of Thibault Formal. A scholar is included among the top collaborators of Thibault Formal 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 Thibault Formal. Thibault Formal is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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9 of 9 papers shown

1.

Déjean, Hervé, et al.. (2024). Retrieval-augmented generation in multilingual settings. 177–188. 6 indexed citations

2.

Formal, Thibault, Stéphane Clinchant, Hervé Déjean, & Carlos Lassance. (2024). SPLATE: Sparse Late Interaction Retrieval. 2635–2640. 3 indexed citations

3.

Déjean, Hervé, et al.. (2024). BERGEN: A Benchmarking Library for Retrieval-Augmented Generation. 7640–7663. 1 indexed citations

4.

Faggioli, Guglielmo, Thibault Formal, Stefano Marchesin, et al.. (2023). Towards Query Performance Prediction for Neural Information Retrieval: Challenges and Opportunities. SPIRE - Sciences Po Institutional REpository. 51–63. 5 indexed citations

5.

Formal, Thibault, Carlos Lassance, Benjamin Piwowarski, & Stéphane Clinchant. (2023). Towards Effective and Efficient Sparse Neural Information Retrieval. ACM Transactions on Information Systems. 42(5). 1–46. 3 indexed citations

6.

Déjean, Hervé, et al.. (2023). Benchmarking Middle-Trained Language Models for Neural Search. 1848–1852. 1 indexed citations

7.

Formal, Thibault, Carlos Lassance, Benjamin Piwowarski, & Stéphane Clinchant. (2022). From Distillation to Hard Negative Sampling. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2353–2359. 50 indexed citations

8.

Formal, Thibault, Benjamin Piwowarski, & Stéphane Clinchant. (2021). SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking. HAL (Le Centre pour la Communication Scientifique Directe). 2288–2292. 128 indexed citations

9.

Lassance, Carlos, Thibault Formal, & Stéphane Clinchant. (2021). Composite Code Sparse Autoencoders for First Stage Retrieval. 2136–2140. 3 indexed citations

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.

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