Papers by Amanda Liu
Not-Just-Scaling Laws: Towards a Better Understanding of the Downstream Impact of Language Model Design Decisions (2025.emnlp-main)
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Emmy Liu, Amanda Bertsch, Lintang Sutawika, Lindia Tjuatja, Patrick Fernandes, Lara Marinov, Michael Chen, Shreya Singhal, Carolin Lawrence, Aditi Raghunathan, Kiril Gashteovski, Graham Neubig
| Challenge: | Language model performance is largely dependent on pretraining decisions, but scaling laws based on only these two aspects do not always explain downstream task performance. |
| Approach: | They meta-analyze 92 open-source pretrained models to quantify their impact on performance. |
| Outcome: | The framework lays a foundation for more systematic investigation of how model development choices shape final capabilities. |
What Else Do I Need to Know? The Effect of Background Information on Users’ Reliance on QA Systems (2023.emnlp-main)
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Navita Goyal, Eleftheria Briakou, Amanda Liu, Connor Baumler, Claire Bonial, Jeffrey Micher, Clare Voss, Marine Carpuat, Hal Daumé III
| Challenge: | Existing NLP systems can only access the retrieved context to determine the answer, resulting in a knowledge gap between the information that is required to answer the question and the information available to assess the model’s correctness. |
| Approach: | They ask whether adding relevant background helps mitigate users’ over-reliance on predictions. |
| Outcome: | The proposed approach reduces over-reliance on model predictions even in the absence of sufficient information to assess their correctness. |