How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements. - Chen Shani; Nadav Borenstein; Dafna Shahaf (ACL/IJCNLP (1), 2021)

Multilingual Event Extraction from Historical Newspaper Adverts. - Nadav Borenstein; Natalia da Silva Perez; Isabelle Augenstein (ACL (1), 2023)

Measuring Intersectional Biases in Historical Documents. - Nadav Borenstein; Karolina Stanczak; Thea Rolskov; Natacha Klein Käfer; Natalia da Silva Perez; Isabelle Augenstein (ACL (Findings), 2023)

PHD: Pixel-Based Language Modeling of Historical Documents. - Nadav Borenstein; Phillip Rust; Desmond Elliott; Isabelle Augenstein (EMNLP, 2023)

Factcheck-GPT: End-to-End Fine-Grained Document-Level Fact-Checking and Correction of LLM Output. - Yuxia Wang; Revanth Gangi Reddy; Zain Muhammad Mujahid; Arnav Arora; Aleksandr Rubashevskii; Jiahui Geng; Osama Mohammed Afzal; Liangming Pan; Nadav Borenstein; Aditya Pillai; Isabelle Augenstein; Iryna Gurevych; et al. (CoRR, 2023)

Imitation of Life: A Search Engine for Biologically Inspired Design. - Hen Emuna; Nadav Borenstein; Xin Qian; Hyeonsu B. Kang; Joel Chan; Aniket Kittur; Dafna Shahaf (AAAI, 2024)

What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages. - Nadav Borenstein; Anej Svete; Robin Chan; Josef Valvoda; Franz Nowak; Isabelle Augenstein; Eleanor Chodroff; Ryan Cotterell (ACL (1), 2024)

LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models. - Dustin Wright 001; Arnav Arora; Nadav Borenstein; Srishti Yadav; Serge J. Belongie; Isabelle Augenstein (EMNLP (Findings), 2024)

Can Transformers Learn n-gram Language Models? - Anej Svete; Nadav Borenstein; Mike Zhou; Isabelle Augenstein; Ryan Cotterell (EMNLP, 2024)

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers. - Yuxia Wang; Revanth Gangi Reddy; Zain Muhammad Mujahid; Arnav Arora; Aleksandr Rubashevskii; Jiahui Geng; Osama Mohammed Afzal; Liangming Pan; Nadav Borenstein; Aditya Pillai; Isabelle Augenstein; Iryna Gurevych; et al. (EMNLP (Findings), 2024)

What Languages are Easy to Language-Model? A Perspective from Learning Probabilistic Regular Languages. - Nadav Borenstein; Anej Svete; Robin Shing Moon Chan; Josef Valvoda; Franz Nowak; Isabelle Augenstein; Eleanor Chodroff; Ryan Cotterell (CoRR, 2024)

Revealing Fine-Grained Values and Opinions in Large Language Models. - Dustin Wright 001; Arnav Arora; Nadav Borenstein; Srishti Yadav; Serge J. Belongie; Isabelle Augenstein (CoRR, 2024)

Can Transformers Learn n-gram Language Models? - Anej Svete; Nadav Borenstein; Mike Zhou; Isabelle Augenstein; Ryan Cotterell (CoRR, 2024)

Investigating Human Values in Online Communities. - Nadav Borenstein; Arnav Arora; Lucie-Aimée Kaffee; Isabelle Augenstein (NAACL (Long Papers), 2025)

Can Community Notes Replace Professional Fact-Checkers? - Nadav Borenstein; Greta Warren; Desmond Elliott; Isabelle Augenstein (CoRR, 2025)

Revisiting Noise in Natural Language Processing for Computational Social Science. - Nadav Borenstein (CoRR, 2025)

BiasGym: Fantastic LLM Biases and How to Find (and Remove) Them. - Sekh Mainul Islam; Nadav Borenstein; Siddhesh Pawar; Haeun Yu; Arnav Arora; Isabelle Augenstein (CoRR, 2025)