Papers by Maximilian Spliethöver
No Word Embedding Model Is Perfect: Evaluating the Representation Accuracy for Social Bias in the Media (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Recent work has relied on word embedding bias measures, such as WEAT, but these methods can be inaccurate due to several representation issues, such low-resource settings and token frequency differences. |
| Approach: | They propose to use WEAT to quantify social bias in US online news articles and embed embedding algorithms to account for the aforementioned issues. |
| Outcome: | The proposed algorithms do not match the literature, but they reduce the gap. |
Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have studied dialect-related fairness for aspects like hate speech, but other aspects of biased language remain unexplored. |
| Approach: | They propose a multitask learning approach that models dialect language as an auxiliary task to incorporate syntactic and lexical variations. |
| Outcome: | The proposed approach improves dialect learning and detects biases more reliably. |
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection (2025.naacl-long)
Copied to clipboard
Maximilian Spliethöver, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik, Barbara Hammer, Eyke Hüllermeier, Henning Wachsmuth
| Challenge: | Existing prompting techniques for large language models depend on several parameters, such as the task, language model, and context provided. |
| Approach: | They propose an adaptive prompting approach that predicts the optimal prompt composition ad-hoc for a given input. |
| Outcome: | The proposed approach ensures high detection performance and is best in several settings. |