Papers by Filip Trhlík
Bias Dynamics in BabyLMs: Towards a Compute-Efficient Sandbox for Democratising Pre-Training Debiasing (2026.findings-acl)
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| Challenge: | Pre-trained language models (LMs) have grown substantially in both societal adoption and training costs. |
| Approach: | They propose to use low-cost proxy models to democratise pre-model debiasing research by using small and mutable corpora. |
| Outcome: | The proposed model can approximate bias acquisition and learning dynamics of larger models despite their reduced size. |
Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles (2024.findings-emnlp)
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| Challenge: | Existing studies focus on LLMs undertaking political questionnaires, which offers only limited insights into their biases and operational nuances. |
| Approach: | They propose to use a curated dataset to generate 56,700 synthetic articles using nine LLMs. |
| Outcome: | The proposed model can detect political biases using supervised models and LLMs. |
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors (2024.acl-long)
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Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu, Daphne Ippolito, Chris Callison-Burch
| Challenge: | Existing methods for detecting machine-generated text are often insufficiently robust and lack benchmark datasets. |
| Approach: | They evaluate the out-of-domain and adversarial robustness of 8 open- and 4 closed-source detectors using RAID benchmark datasets. |
| Outcome: | The proposed detectors are fooled by adversarial attacks, repetition penalties, and unseen generative models. |