Papers by Hagen Blix
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
On the Machine Learning of Ethical Judgments from Natural Language (2022.naacl-main)
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| Challenge: | a recent study examines the morality of NLP models that can take in arbitrary text and output a moral judgment . a Delphi project is a popular system for moral prediction, but it has received criticism . |
| Approach: | They propose to critique NLP methods for automating ethical decision-making . they examine a nascent task of predicting moral and ethical decisions from text . |
| Outcome: | The proposed model is unsafe at any accuracy, the authors argue . they argue that the proposed model could be useful in NLP, but not in AI. |
Predicting Declension Class from Form and Meaning (2020.acl-main)
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| Challenge: | phonological form and meaning of nouns can provide imperfect clues, but they can also be indicative of grammatical gender. |
| Approach: | They propose a method to measure how much information can be gleamed from knowing the form and/or meaning of nouns. |
| Outcome: | The proposed method provides additional quantitative support for a classic linguistic finding that form and meaning are relevant for the classification of nouns into declensions. |
Domain Regeneration: How well do LLMs match syntactic properties of text domains? (2025.findings-acl)
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| Challenge: | Recent improvements in large language models have improved their ability to approximate distributions . authors find that LLMs can suffer from model collapse due to domain considerations based on pretraining . |
| Approach: | They use open source LLMs to regenerate permissively licensed English text from Wikipedia and news text. |
| Outcome: | The proposed model can faithfully match the human-generated distributions in a semantically-controlled setting. |