• 10/2024: Giving a talk on AI dialect prejudice at the SESP Annual Conference
• 09/2024: Our work on dialect prejudice was featured in Science News, ScienceNews, and PNAS Front Matter
• 08/2024: Our paper on dialect prejudice in language models was published as an article in Nature
• 08/2024: Two awards at ACL 2024: Outstanding Paper for our work on evaluating political values in language models, and Best Resource Paper for Dolma
• 05/2024: Our papers on evaluating political values in language models and the Dolma corpus were both accepted to ACL 2024
• 05/2024: Our paper on graph-enhanced language models was accepted to ICML 2024
• 03/2024: Our work on AI dialect prejudice was featured in The Guardian, Sky News, New Scientist, MIT Tech Review, The Register, and Nature News
• 03/2024: New preprint on AI dialect prejudice against speakers of African American English
• 02/2024: New preprint on evaluating political values in language models
• 02/2024: New preprint on graph-enhanced language models for asynchronous plan reasoning
• 02/2024: Giving a talk on geographically grounded language models at University of Cambridge’s Language Technology Lab
• 02/2024: New preprint on the Dolma corpus
• 01/2024: Our paper on measuring and improving the geolinguistic knowledge of language models was published as an article in TACL

2024

AI generates covertly racist decisions about people based on their dialect
Valentin Hofmann, Pratyusha Ria Kalluri, Dan Jurafsky, and Sharese King
Nature

MAGNET: Improving the multilingual fairness of language models with adaptive gradient-based tokenization
Orevaoghene Ahia, Sachin Kumar, Hila Gonen, Valentin Hofmann, Tomasz Limisiewicz, Yulia Tsvetkov, and Noah Smith
NeurIPS 2024

Paloma: A benchmark for evaluating language model fit
Ian Magnusson, Akshita Bhagia, Valentin Hofmann, Luca Soldaini, Ananya Harsh Jha, Oyvind Tafjord, Dustin Schwenk, Evan Pete Walsh, Yanai Elazar, Kyle Lo, Dirk Groeneveld, Iz Beltagy, Hannaneh Hajishirzi, Noah Smith, Kyle Richardson, and Jesse Dodge
NeurIPS 2024

Political compass or spinning arrow? Towards more meaningful evaluations for values and opinions in large language models
Paul Röttger*, Valentin Hofmann*, Valentina Pyatkin, Musashi Hinck, Hannah Kirk, Hinrich Schütze, and Dirk Hovy
ACL 2024
🏆 Outstanding Paper Award

Dolma: An open corpus of three trillion tokens for language model pretraining research
Luca Soldaini, Rodney Kinney, Akshita Bhagia, Dustin Schwenk, David Atkinson, Russell Authur, Ben Bogin, Khyathi Chandu, Jennifer Dumas, Yanai Elazar, Valentin Hofmann, Ananya Harsh Jha, Sachin Kumar, Li Lucy, Xinxi Lyu, Nathan Lambert, Ian Magnusson, Jacob Morrison, Niklas Muennighoff, Aakanksha Naik, Crystal Nam, Matthew Peters, Abhilasha Ravichander, Kyle Richardson, Zejiang Shen, Emma Strubell, Nishant Subramani, Oyvind Tafjord, Pete Walsh, Luke Zettlemoyer, Noah Smith, Hannaneh Hajishirzi, Iz Beltagy, Dirk Groeneveld, Jesse Dodge, and Kyle Lo
ACL 2024
🏆 Best Resource Paper Award

Graph-enhanced large language models in asynchronous plan reasoning
Fangru Lin, Emanuele La Malfa, Valentin Hofmann, Elle Michelle Yang, Anthony Cohn, and Janet Pierrehumbert
ICML 2024

Geographic adaptation of pretrained language models
Valentin Hofmann, Goran Glavas, Nikola Ljubesic, Janet Pierrehumbert, and Hinrich Schütze
TACL

2023

Counting the bugs in ChatGPT's wugs: A multilingual investigation into the morphological capabilities of a large language model
Leonie Weissweiler*, Valentin Hofmann*, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schütze, Kemal Oflazer, and David Mortensen
EMNLP 2023

Explaining pretrained language models' understanding of linguistic structures using construction grammar
Leonie Weissweiler, Valentin Hofmann, Abdullatif Köksal, and Hinrich Schütze
Frontiers in Artificial Intelligence

2022

The better your syntax, the better your semantics? Probing pretrained language models for the English comparative correlative
Leonie Weissweiler, Valentin Hofmann, Abdullatif Köksal, and Hinrich Schütze
EMNLP 2022

Unsupervised detection of contextualized embedding bias with application to ideology
Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze
ICML 2022

Modeling ideological salience and framing in polarized online groups with graph neural networks and structured sparsity
Valentin Hofmann, Xiaowen Dong, Janet Pierrehumbert, and Hinrich Schütze
NAACL 2022 (Findings)

The Reddit Politosphere: A large-scale text and network resource of online political discourse
Valentin Hofmann, Hinrich Schütze, and Janet Pierrehumbert
ICWSM 2022

An embarrassingly simple method to mitigate undesirable properties of pretrained language model tokenizers
Valentin Hofmann, Hinrich Schütze, and Janet Pierrehumbert
ACL 2022

CaMEL: Case marker extraction without labels
Leonie Weissweiler, Valentin Hofmann, Masoud Jalili Sabet, and Hinrich Schütze
ACL 2022

2021

Superbizarre is not superb: Derivational morphology improves BERT’s interpretation of complex words
Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze
ACL 2021

Dynamic contextualized word embeddings
Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze
ACL 2021

2020

DagoBERT: Generating derivational morphology with a pretrained language model
Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze
EMNLP 2020

Predicting the growth of morphological families from social and linguistic factors
Valentin Hofmann, Janet Pierrehumbert, and Hinrich Schütze
ACL 2020

A graph auto-encoder model of derivational morphology
Valentin Hofmann, Hinrich Schütze, and Janet Pierrehumbert
ACL 2020