Papers by Valentin Hofmann
Dynamic Contextualized Word Embeddings (2021.acl-long)
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| Challenge: | Static word embeddings that represent words by a single vector cannot capture word meaning in different linguistic and extralinguistic contexts. |
| Approach: | They propose dynamic contextualized word embeddings that represent words as a function of linguistic and extralinguistic contexts. |
| Outcome: | The proposed model models time and social space jointly, making them attractive for NLP tasks involving semantic variability. |
Modeling Ideological Salience and Framing in Polarized Online Groups with Graph Neural Networks and Structured Sparsity (2022.findings-naacl)
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| Challenge: | Existing methods to detect ideological divides in social media rely on knowing in advance the political orientation of text . fascist and mainstream are among the most polarized concepts in reddit in 2019 . |
| Approach: | They propose a minimally supervised method that leverages the network structure of online discussion forums to detect polarized concepts. |
| Outcome: | The proposed framework captures temporal ideological dynamics such as right-wing and left-wing radicalization using graph neural networks and sparsity learning. |
Predicting the Growth of Morphological Families from Social and Linguistic Factors (2020.acl-main)
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| Challenge: | a burst in token frequency of the word "trump" in social media before the 2016 presidential election is a prime indicator of topical dynamics. |
| Approach: | They propose a task of Morphological Family Expansion Prediction to predict the size of a morphological family by analyzing a reddit corpus. |
| Outcome: | The proposed task predicts the increase in the size of a morphological family on a reddit corpus. |
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance (2026.tacl-1)
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Paul Röttger, Musashi Hinck, Valentin Hofmann, Kobi Hackenburg, Valentina Pyatkin, Faeze Brahman, Dirk Hovy
| Challenge: | Large language models are helping millions of users write texts about diverse issues . issue bias is where an LLM tends to present just one perspective on a given issue . |
| Approach: | They construct a set of 2.49m realistic English-language prompts to measure issue bias in LLM writing assistance using 3.9k templates and 212 political issues from real user interactions. |
| Outcome: | The proposed model aligns more with US Democrat than Republican voter opinion on a subset of issues. |
Superbizarre Is Not Superb: Derivational Morphology Improves BERT’s Interpretation of Complex Words (2021.acl-long)
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| Challenge: | Pretrained language models (PLMs) are based on fixed-size vocabularies of words and subwords that are generated by compression algorithms such as bytepair encoding. |
| Approach: | They propose to use BERT as an example PLM to study its semantic representations of English derivatives to test their hypothesis. |
| Outcome: | The proposed model outperforms BERT on a series of semantic probing tasks. |
Assessing Dialect Fairness and Robustness of Large Language Models in Reasoning Tasks (2025.acl-long)
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Fangru Lin, Shaoguang Mao, Emanuele La Malfa, Valentin Hofmann, Adrian de Wynter, Xun Wang, Si-Qing Chen, Michael J. Wooldridge, Janet B. Pierrehumbert, Furu Wei
| Challenge: | a study aims to assess the fairness and robustness of Large Language Models in dialectal queries . speakers of "non-standard" dialects are known to experience implicit and explicit discrimination . |
| Approach: | They propose to use a benchmark to assess the fairness of large language models in dialects . they hire speakers with computer science backgrounds to rewrite seven popular benchmarks based on AAVE . |
| Outcome: | The proposed benchmarks show that most models show significant brittleness and unfairness to queries in AAVE. |
Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race (2025.acl-long)
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| Challenge: | et al., 2012) show value-aligned language models exhibit stereotypes in word association tasks . ignoring racial nuances can perpetuate subtle biases in LMs . |
| Approach: | They propose a bias mitigation strategy that incentivizes representation of racial concepts in early model layers. |
| Outcome: | The proposed approach incentivizes representation of racial concepts in early model layers . it reduces implicit bias by reducing the number of ambiguous inputs, the authors show . |
An Embarrassingly Simple Method to Mitigate Undesirable Properties of Pretrained Language Model Tokenizers (2022.acl-short)
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| Challenge: | a standard tokenizer does not cover all characters of a word but preserves key aspects of its morphological structure . a novel method to improve tokenization of pretrained language models is proposed . |
| Approach: | They propose a method to improve the tokenization of pretrained language models . they use the vocabulary of a standard tokenizer but preserves morphological structure . |
| Outcome: | The proposed method improves tokenization of pretrained language models on morphological gold segmentations and text classification tasks. |
Large Language Models Discriminate Against Speakers of German Dialects (2025.emnlp-main)
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| Challenge: | In Germany, more than 40% of the population speaks a regional dialect . however, dialect speakers face negative societal stereotypes . |
| Approach: | They construct a corpus that pairs sentences from seven regional German dialects with their standard German counterparts to assess their dialect usage bias. |
| Outcome: | The proposed model reproduces dialect usage bias in association task and decision task. |
A Graph Auto-encoder Model of Derivational Morphology (2020.acl-main)
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| Challenge: | Existing words that conform to morphological patterns of a language differ in how likely they are to be actually created by speakers. |
| Approach: | They propose to model the morphological well-formedness of derivatives by combining syntactic and semantic information with associative information from the mental lexicon. |
| Outcome: | The proposed model models the morphological well-formedness of derivatives in English . |
The better your Syntax, the better your Semantics? Probing Pretrained Language Models for the English Comparative Correlative (2022.emnlp-main)
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| Challenge: | Construction Grammar posits constructions as the central building blocks of language . human-like performance of pretrained language models on many NLP tasks has been alleged . |
| Approach: | They propose to use construction grammar to posit constructions as the central building blocks of language . they conduct experiments with three pretrained language models to examine their ability to classify and understand English comparative correlative . |
| Outcome: | The proposed models are able to recognise the English comparative correlative (CC) but fail to use its meaning. |
Counting the Bugs in ChatGPT’s Wugs: A Multilingual Investigation into the Morphological Capabilities of a Large Language Model (2023.emnlp-main)
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Leonie Weissweiler, Valentin Hofmann, Anjali Kantharuban, Anna Cai, Ritam Dutt, Amey Hengle, Anubha Kabra, Atharva Kulkarni, Abhishek Vijayakumar, Haofei Yu, Hinrich Schuetze, Kemal Oflazer, David Mortensen
| Challenge: | Existing studies on large language models (LLMs) ignore the remarkable ability of humans to generalize and focus only on English. |
| Approach: | They conduct the first rigorous analysis of the morphological capabilities of ChatGPT in four typologically varied languages. |
| Outcome: | The proposed model massively underperforms purpose-built systems, particularly in English. |
CaMEL: Case Marker Extraction without Labels (2022.acl-long)
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| Challenge: | Existing models for morphological case marking and semantic content are not isomorphic. |
| Approach: | They propose a model that extracts case markers from a multilingual corpus using a noun phrase chunker and an alignment system. |
| Outcome: | The proposed model can extract case markers in 83 languages and visualise similarities and differences between case systems and annotate fine-grained deep cases in languages where they are not overtly marked. |
DagoBERT: Generating Derivational Morphology with a Pretrained Language Model (2020.emnlp-main)
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| Challenge: | Pretrained language models (PLMs) generate derivationally complex words, but it is unclear what they learn about other aspects of language. |
| Approach: | They propose to use BERT to examine its derivational capabilities in different settings, from unmodified pretrained models to full finetuning. |
| Outcome: | The proposed model outperforms the state-of-the-art in derivation generation. |