Philippe Laban

712 total citations · 1 hit paper
28 papers, 310 citations indexed

About

Philippe Laban is a scholar working on Artificial Intelligence, Information Systems and Computer Vision and Pattern Recognition. According to data from OpenAlex, Philippe Laban has authored 28 papers receiving a total of 310 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Artificial Intelligence, 6 papers in Information Systems and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Philippe Laban's work include Topic Modeling (16 papers), Natural Language Processing Techniques (16 papers) and Text Readability and Simplification (4 papers). Philippe Laban is often cited by papers focused on Topic Modeling (16 papers), Natural Language Processing Techniques (16 papers) and Text Readability and Simplification (4 papers). Philippe Laban collaborates with scholars based in United States. Philippe Laban's co-authors include Marti A. Hearst, Tobias Schnabel, Paul N. Bennett, Chien-Sheng Wu, Caiming Xiong, Tuhin Chakrabarty, Smaranda Muresan, Wenhao Liu, Greg Durrett and Liyan Tang and has published in prestigious journals such as ACM Transactions on Computer-Human Interaction and Transactions of the Association for Computational Linguistics.

In The Last Decade

Philippe Laban

25 papers receiving 300 citations

Hit Papers

align trajectories

What are hit papers?

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.

Art or Artifice? Large Language Models and the False Promise of Creativity

2024 45 citations Tuhin Chakrabarty, Philippe Laban et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Philippe Laban United States	9	242	34	31	19	15	28	310
Emmanuele Chersoni Hong Kong	12	226 0.9×	17 0.5×	17 0.5×	16 0.8×	31 2.1×	54	326
Anna Rogers United States	11	299 1.2×	26 0.8×	67 2.2×	25 1.3×	13 0.9×	23	370
Silvio Amir Portugal	8	486 2.0×	50 1.5×	62 2.0×	12 0.6×	35 2.3×	17	539
Marc Franco-Salvador Spain	12	314 1.3×	60 1.8×	15 0.5×	10 0.5×	4 0.3×	24	358
Dimitra Gkatzia United Kingdom	9	202 0.8×	28 0.8×	48 1.5×	12 0.6×	6 0.4×	33	248
Yada Pruksachatkun United States	7	301 1.2×	31 0.9×	80 2.6×	35 1.8×	12 0.8×	8	372
Yanai Elazar Israel	11	433 1.8×	37 1.1×	95 3.1×	20 1.1×	13 0.9×	22	505
Lamia Hadrich Belguith Tunisia	14	633 2.6×	59 1.7×	23 0.7×	24 1.3×	9 0.6×	133	696
Alexey Romanov United States	8	341 1.4×	32 0.9×	65 2.1×	18 0.9×	56 3.7×	17	397
Christos Christodoulopoulos United Kingdom	11	355 1.5×	67 2.0×	36 1.2×	47 2.5×	18 1.2×	29	429

Countries citing papers authored by Philippe Laban

Since Specialization

Citations

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

Fields of papers citing papers by Philippe Laban

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philippe Laban

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Laban, Philippe, et al.. (2025). Why Vision Language Models Struggle with Visual Arithmetic? Towards Enhanced Chart and Geometry Understanding. 4830–4843.

2.

Spangher, Alexander, et al.. (2025). Creative Planning with Language Models: Practice, Evaluation and Applications. 1–9.

3.

Laban, Philippe, et al.. (2025). Do RAG Systems Cover What Matters? Evaluating and Optimizing Responses with Sub-Question Coverage. 5836–5849. 1 indexed citations

4.

Tang, Liyan, Philippe Laban, & Greg Durrett. (2024). MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents. 8818–8847. 8 indexed citations

5.

Laban, Philippe, Alexander R. Fabbri, Prafulla Kumar Choubey, et al.. (2024). Embrace Divergence for Richer Insights: A Multi-document Summarization Benchmark and a Case Study on Summarizing Diverse Information from News Articles. 570–593. 1 indexed citations

6.

Dou, Yao, Philippe Laban, Claire Gardent, & Wei Xu. (2024). Automatic and Human-AI Interactive Text Generation (with a focus on Text Simplification and Revision). 3–4. 1 indexed citations

7.

Laban, Philippe, Jesse Vig, Marti A. Hearst, Caiming Xiong, & Chien-Sheng Wu. (2024). Beyond the Chat: Executable and Verifiable Text-Editing with LLMs. 1–23. 6 indexed citations

8.

Laban, Philippe, Alexander R. Fabbri, Caiming Xiong, & Chien-Sheng Wu. (2024). Summary of a Haystack: A Challenge to Long-Context LLMs and RAG Systems. 9885–9903. 6 indexed citations

9.

Agarwal, Divyansh, et al.. (2024). Prompt Leakage effect and mitigation strategies for multi-turn LLM Applications. 1255–1275. 4 indexed citations

10.

Chakrabarty, Tuhin, et al.. (2024). Art or Artifice? Large Language Models and the False Promise of Creativity. 1–34. 45 indexed citations breakdown →

11.

Laban, Philippe, et al.. (2023). Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems. 9823–9838. 2 indexed citations

12.

Tang, Liyan, Tanya Goyal, Philippe Laban, et al.. (2023). Understanding Factual Errors in Summarization: Errors, Summarizers, Datasets, Error Detectors. 11626–11644. 21 indexed citations

13.

Yin, Fan, Jesse Vig, Philippe Laban, et al.. (2023). Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning. 3063–3079. 5 indexed citations

14.

Laban, Philippe, Jesse Vig, Wojciech Kryściński, et al.. (2023). SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages. 10674–10695. 5 indexed citations

15.

Laban, Philippe, Wojciech Kryściński, Divyansh Agarwal, et al.. (2023). SummEdits: Measuring LLM Ability at Factual Reasoning Through The Lens of Summarization. 9662–9676. 12 indexed citations

16.

Laban, Philippe, et al.. (2023). Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions. 1–21. 3 indexed citations

17.

Wu, Chien-Sheng, et al.. (2022). MixQG: Neural Question Generation with Mixed Answer Types. 1486–1497. 21 indexed citations

18.

Laban, Philippe, Chien-Sheng Wu, Wenhao Liu, & Caiming Xiong. (2022). Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets. 2094–2108. 3 indexed citations

19.

Laban, Philippe, et al.. (2022). Discord Questions: A Computational Approach To Diversity Analysis in News Coverage. 5180–5194. 1 indexed citations

20.

Laban, Philippe & Marti A. Hearst. (2017). newsLens: building and visualizing long-ranging news stories. 1–9. 19 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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