Papers by Prasann Singhal
Assessing Out-of-Domain Language Model Performance from Few Examples (2023.eacl-main)
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| Challenge: | Pretrained language models exhibit impressive generalization capabilities, but behave unpredictably under certain domain shifts. |
| Approach: | They propose to incorporate attributions into a few-shot model predicting out-of-domain (OOD) performance task to find out if models agree with pathological heuristics that may indicate worse generalization capabilities. |
| Outcome: | The proposed model-based model-learning model can perform better on a few-shot example set, and incorporate feature attributions to improve it. |
EEL: Efficiently Encoding Lattices for Reranking (2023.acl-long)
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| Challenge: | Existing methods for decoding conditional text are slow to apply to large numbers of hypotheses. |
| Approach: | They propose a method that can efficiently encode lattices of generated outputs using Transformers. |
| Outcome: | The proposed method can extract high-quality hypotheses from lattices with minimal degradation error compared to naive reranking methods. |