Papers by Nadav Borenstein
Multilingual Event Extraction from Historical Newspaper Adverts (2023.acl-long)
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| Challenge: | Developing NLP methods for historical corpora is difficult, as only domain experts can label them . off-the-shelf models are trained on modern language texts, rendering them weaker for historical documents . |
| Approach: | They propose to use an annotated newspaper dataset to extract historical data from a novel domain of texts. |
| Outcome: | The proposed method performs well on a multilingual dataset in English, French, and Dutch . it is possible to extract surprisingly good results even with scarce annotated data using existing models and datasets for modern languages . |
Measuring Intersectional Biases in Historical Documents (2023.findings-acl)
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Nadav Borenstein, Karolina Stanczak, Thea Rolskov, Natacha Klein Käfer, Natália da Silva Perez, Isabelle Augenstein
| Challenge: | digitised historical documents suffer from errors introduced by optical character recognition (OCR) and are written in an archaic language. |
| Approach: | They investigate the continuities and transformations of bias in Caribbean historical newspapers during the colonial era . they use distributional semantics models and word embeddings to measure gender, race, and intersectional biases. |
| Outcome: | The authors show that gender and racial biases are interdependent and their intersection triggers distinct effects. |
Can Community Notes Replace Professional Fact-Checkers? (2025.acl-short)
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| Challenge: | Fact-checkers are crucial in combating misinformation on social media . however, community moderation is often employed in parallel due to the scale of misleading content shared online. |
| Approach: | They use language models to annotate Twitter/X community notes with attributes such as topic, cited sources, and whether they refute misinformation claims. |
| Outcome: | The results show that community notes cite fact-checking sources up to five times more than previously reported. |
Investigating Human Values in Online Communities (2025.naacl-long)
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| Challenge: | Existing value frameworks struggle with sample sizes and rely on selfreported surveys to calculate values. |
| Approach: | They propose a method to computationally analyse values on Reddit using in-domain and out-of-domain human annotations to train a value relevance and a polarity classifier. |
| Outcome: | The proposed method can be used to analyse values on reddit using human annotations and human annotation. |
Can Transformers Learn n-gram Language Models? (2024.emnlp-main)
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| Challenge: | Existing work has tested transformers' ability to represent formal languages, but language models are not classifiers of strings but rather distributions over them. |
| Approach: | They relate transformers' ability to learn random n-gram language models to ngram language model (LM) they find add- smoothing outperforms transformers on the former, while transformers perform better on the latter . |
| Outcome: | The proposed models outperform classical methods designed to learn n-gram LMs, while transformers perform better on the latter. |
Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers (2024.findings-emnlp)
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Yuxia Wang, Revanth Gangi Reddy, Zain Mujahid, Arnav Arora, Aleksandr Rubashevskii, Jiahui Geng, Osama Mohammed Afzal, Liangming Pan, Nadav Borenstein, Aditya Pillai, Isabelle Augenstein, Iryna Gurevych, Preslav Nakov
| Challenge: | Large language models generate naturally sounding answers over a broad range of human inquiries, but they often generate answers that contradict real-world facts. |
| Approach: | They propose a framework for annotating and evaluating the factuality of large language models . they propose 'factcheck-bench' which provides a multi-stage annotation scheme . |
| Outcome: | The proposed framework outperforms several popular LLM fact-checkers in claim, sentence, and document levels. |
PHD: Pixel-Based Language Modeling of Historical Documents (2023.emnlp-main)
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| Challenge: | Recent years have seen a boom in efforts to digitise historical documents in numerous languages and sources, leading to a transformation in the way historians work. |
| Approach: | They propose a method for generating synthetic scans to resemble real historical documents by pre-training a model to reconstruct masked patches instead of predicting token distributions. |
| Outcome: | The proposed model can reconstruct masked patches and understand language well. |
LLM Tropes: Revealing Fine-Grained Values and Opinions in Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing approaches to evaluate latent values and opinions in large language models suffer from three notable shortcomings. |
| Approach: | They propose to analyze 156k LLM responses to 62 propositions of the Political Compass Test (PCT) generated by 6 LLMs using 420 prompt variations. |
| Outcome: | The proposed analysis of 156k LLM responses to the Political Compass Test (PCT) generated by 6 LLMs shows that tropes are recurrent and consistent across prompts. |
How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements (2021.acl-long)
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| Challenge: | Humor is an important social phenomenon, serving complex social and psychological functions. |
| Approach: | They propose a novel algorithm for automatically detecting funny scientific papers . they use a dataset containing thousands of funny papers to learn classifiers . |
| Outcome: | The proposed task is based on a dataset containing thousands of funny scientific papers . it is a novel task that can be automated and improves on existing methods . |