Faisal Ladhak

3.7k total citations · 3 hit papers
14 papers, 443 citations indexed

About

Faisal Ladhak is a scholar working on Artificial Intelligence, Sociology and Political Science and Computer Vision and Pattern Recognition. According to data from OpenAlex, Faisal Ladhak has authored 14 papers receiving a total of 443 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 2 papers in Sociology and Political Science and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Faisal Ladhak's work include Topic Modeling (11 papers), Natural Language Processing Techniques (10 papers) and Advanced Text Analysis Techniques (6 papers). Faisal Ladhak is often cited by papers focused on Topic Modeling (11 papers), Natural Language Processing Techniques (10 papers) and Advanced Text Analysis Techniques (6 papers). Faisal Ladhak collaborates with scholars based in United States, Slovakia and Italy. Faisal Ladhak's co-authors include Esin Durmus, Tatsunori Hashimoto, Kathleen McKeown, Tianyi Zhang, Percy Liang, Dan Jurafsky, Aylin Caliskan, Debora Nozza, Federico Bianchi and Pratyusha Kalluri and has published in prestigious journals such as Transactions of the Association for Computational Linguistics, Rare & Special e-Zone (The Hong Kong University of Science and Technology) and Edinburgh Research Explorer.

In The Last Decade

Faisal Ladhak

13 papers receiving 418 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.

Benchmarking Large Language Models for News Summarization

2024 124 citations Tianyi Zhang, Faisal Ladhak et al. Transactions of the Association for Computational Linguistics profile →
Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale

2023 118 citations Federico Bianchi, Pratyusha Kalluri et al. BOA (University of Milano-Bicocca) profile →
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference

2025 14 citations Antoine Chaffin, Orion Weller et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Faisal Ladhak United States	9	303	74	37	29	27	14	443
Esin Durmus United States	10	280 0.9×	51 0.7×	40 1.1×	27 0.9×	45 1.7×	18	432
Yanai Elazar Israel	11	433 1.4×	95 1.3×	27 0.7×	15 0.5×	20 0.7×	22	505
Rishi Bommasani United States	7	330 1.1×	36 0.5×	34 0.9×	59 2.0×	19 0.7×	15	435
Kyle McDonell United Kingdom	2	247 0.8×	39 0.5×	16 0.4×	37 1.3×	20 0.7×	2	391
Laria Reynolds United States	3	247 0.8×	39 0.5×	16 0.4×	37 1.3×	20 0.7×	3	394
Ilia Shumailov United Kingdom	8	188 0.6×	38 0.5×	40 1.1×	33 1.1×	53 2.0×	19	396
Anna Rogers United States	11	299 1.0×	67 0.9×	14 0.4×	10 0.3×	25 0.9×	23	370
Nancy Fulda United States	6	279 0.9×	39 0.5×	36 1.0×	16 0.6×	76 2.8×	22	455
Nan‐Chen Chen United States	7	139 0.5×	71 1.0×	50 1.4×	16 0.6×	34 1.3×	11	280
Holy Lovenia Hong Kong	6	286 0.9×	34 0.5×	17 0.5×	79 2.7×	31 1.1×	13	431

Countries citing papers authored by Faisal Ladhak

Since Specialization

Citations

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

Fields of papers citing papers by Faisal Ladhak

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Faisal Ladhak

This figure shows the co-authorship network connecting the top 25 collaborators of Faisal Ladhak. A scholar is included among the top collaborators of Faisal Ladhak 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 Faisal Ladhak. Faisal Ladhak 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:

14 of 14 papers shown

Chaffin, Antoine, et al.. (2025). Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference. 2526–2547. 14 indexed citations breakdown →

Subbiah, Melanie, et al.. (2024). STORYSUMM: Evaluating Faithfulness in Story Summarization. 9988–10005.

Zhang, Tianyi, Faisal Ladhak, Esin Durmus, et al.. (2024). Benchmarking Large Language Models for News Summarization. Transactions of the Association for Computational Linguistics. 12. 39–57. 124 indexed citations breakdown →

Ladhak, Faisal, et al.. (2023). From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting. PubMed Central. 68–74. 18 indexed citations

Fabbri, Alexander R., et al.. (2023). Generating EDU Extracts for Plan-Guided Summary Re-Ranking. PubMed. 2023. 2680–2697. 3 indexed citations

Bianchi, Federico, Pratyusha Kalluri, Esin Durmus, et al.. (2023). Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale. BOA (University of Milano-Bicocca). 1493–1504. 118 indexed citations breakdown →

Ladhak, Faisal, Esin Durmus, Mirac Süzgün, et al.. (2023). When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization. 3206–3219. 15 indexed citations

Wang, Tianshu, Faisal Ladhak, Esin Durmus, & He He. (2022). Improving Faithfulness by Augmenting Negative Summaries from Fake Documents. 11913–11921. 3 indexed citations

Ladhak, Faisal, Srinivasan Iyer, Veselin Stoyanov, et al.. (2022). ToKen: Task Decomposition and Knowledge Infusion for Few-Shot Hate Speech Detection. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2109–2120. 8 indexed citations

10.

Ladhak, Faisal, Esin Durmus, He He, Claire Cardie, & Kathleen McKeown. (2022). Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1410–1421. 38 indexed citations

11.

Durmus, Esin, Faisal Ladhak, & Tatsunori Hashimoto. (2022). Spurious Correlations in Reference-Free Evaluation of Text Generation. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 1443–1454. 11 indexed citations

12.

Wan, David, et al.. (2021). Segmenting Subtitles for Correcting ASR Segmentation Errors. Edinburgh Research Explorer. 2842–2854. 3 indexed citations

13.

Lu, Yichao, et al.. (2018). A neural interlingua for multilingual machine translation. 84–92. 50 indexed citations

14.

Ladhak, Faisal, Ankur Gandhe, Markus Dreyer, et al.. (2016). LatticeRnn: Recurrent Neural Networks Over Lattices. 695–699. 38 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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