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Reading Between the Lines: Classifying Resume Seniority with Large Language Models
Matan Cohen, Shira Shani, Eden Menahem, Yehudit Aperstein, Alexander Apartsin
https://arxiv.org/abs/2509.09229

Reading Between the Lines: Classifying Resume Seniority with Large Language Models
Accurately assessing candidate seniority from resumes is a critical yet challenging task, complicated by the prevalence of overstated experience and ambiguous self-presentation. In this study, we investigate the effectiveness of large language models (LLMs), including fine-tuned BERT architectures, for automating seniority classification in resumes. To rigorously evaluate model performance, we introduce a hybrid dataset comprising both real-world resumes and synthetically generated hard example…

Optimal Quoting under Adverse Selection and Price Reading
Alexander Barzykin, Philippe Bergault, Olivier Gu\'eant, Malo Lemmel
https://arxiv.org/abs/2508.20225 https://

Optimal Quoting under Adverse Selection and Price Reading
Over the past decade, many dealers have implemented algorithmic models to automatically respond to RFQs and manage flows originating from their electronic platforms. In parallel, building on the foundational work of Ho and Stoll, and later Avellaneda and Stoikov, the academic literature on market making has expanded to address trade size distributions, client tiering, complex price dynamics, alpha signals, and the internalization versus externalization dilemma in markets with dealer-to-client a…

CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
Brian Yan, Injy Hamed, Shuichiro Shimizu, Vasista Lodagala, William Chen, Olga Iakovenko, Bashar Talafha, Amir Hussein, Alexander Polok, Kalvin Chang, Dominik Klement, Sara Althubaiti, Puyuan Peng, Matthew Wiesner, Thamar Solorio, Ahmed Ali, Sanjeev Khudanpur, Shinji Watanabe, Chih-Chen Chen, Zhen Wu, Karim Benharrak, Anuj Diwan, Samuele Cornell, Eunjung Yeo, Kwanghee Choi, Carlos Carvalho, Karen Rosero

CS-FLEURS: A Massively Multilingual and Code-Switched Speech Dataset
We present CS-FLEURS, a new dataset for developing and evaluating code-switched speech recognition and translation systems beyond high-resourced languages. CS-FLEURS consists of 4 test sets which cover in total 113 unique code-switched language pairs across 52 languages: 1) a 14 X-English language pair set with real voices reading synthetically generated code-switched sentences, 2) a 16 X-English language pair set with generative text-to-speech 3) a 60 {Arabic, Mandarin, Hindi, Spanish}-X langu…