Papers by Katrin Tomanek
Detecting Hallucination and Coverage Errors in Retrieval Augmented Generation for Controversial Topics (2024.lrec-main)
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Tyler A. Chang, Katrin Tomanek, Jessica Hoffmann, Nithum Thain, Erin MacMurray van Liemt, Kathleen Meier-Hellstern, Lucas Dixon
| Challenge: | a growing audience of users is engaging with LLM-driven chatbots. |
| Approach: | They propose a strategy to handle controversial topics in LLM-based chatbots based on Wikipedia’s Neutral Point of View principle. |
| Outcome: | The proposed methods detect errors in the tuned LLM responses even when no training data is available. |
Towards Agile Text Classifiers for Everyone (2023.findings-emnlp)
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Maximilian Mozes, Jessica Hoffmann, Katrin Tomanek, Muhamed Kouate, Nithum Thain, Ann Yuan, Tolga Bolukbasi, Lucas Dixon
| Challenge: | Text-based safety classifiers are widely used for content moderation and increasingly to tune generative language model behavior. |
| Approach: | They propose to use small, targeted datasets to train safety classifiers using small, iterative datasets that can be quickly developed for a particular policy. |
| Outcome: | The proposed method can be quickly developed for a specific policy with a labeled dataset of as few as 80 examples. |
Residual Adapters for Parameter-Efficient ASR Adaptation to Atypical and Accented Speech (2021.emnlp-main)
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| Challenge: | Automatic Speech Recognition systems perform poorly on atypical speech and heavily accented speech. |
| Approach: | They add a residual adapter to the encoder layer to improve model adaptation . they show that the residual adapters update only a tiny fraction of the model parameters . |
| Outcome: | The proposed model fine-tuning improves performance on atypical and accented speech . the system can update only a tiny fraction of the model parameters . |
Context-Aware Abbreviation Expansion Using Large Language Models (2022.naacl-main)
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Shanqing Cai, Subhashini Venugopalan, Katrin Tomanek, Ajit Narayanan, Meredith Morris, Michael Brenner
| Challenge: | Existing paradigms for text entry in augmentative and alternative communication (AAC) for people with severe motor impairments require 3-5 predictions to save keystrokes. |
| Approach: | They propose a paradigm in which phrases are abbreviated aggressively as word-initial letters. |
| Outcome: | The proposed paradigm can save up to 77% on expansions on conversation turn . the proposed paradigm could be used in augmentative and alternative communication (AAC) |
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey (2026.eacl-long)
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| Challenge: | a longstanding strategy to reduce annotation costs is active learning . data annotation is expected to remain important and active learning to stay relevant . |
| Approach: | They conduct an online survey to assess the perceived relevance of data annotation and active learning . they propose a strategy to reduce annotation costs using active learning, an iterative process . |
| Outcome: | The proposed strategies reduce setup complexity and uncertainty cost while maintaining model performance. |