Papers by Benjie Wang
Assessing Robustness of Text Classification through Maximal Safe Radius Computation (2020.findings-emnlp)
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| Challenge: | Neural network NLP models are vulnerable to small modifications of the input that maintain the original meaning but result in a different prediction. |
| Approach: | They propose to provide a measure of robustness against word substitutions by computing a safe radius for a given input text. |
| Outcome: | The proposed methods are compared with LIME and CNN-Cert and show that they perform well on sentiment analysis and news classification models. |
Where is the signal in tokenization space? (2024.emnlp-main)
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| Challenge: | Autoregressive large language models (LLMs) generate text by predicting the next word sequentially, but tokenization is a significant challenge. |
| Approach: | They propose to use a deterministic, rule-based mapping from text to token sequences to compute the marginal probability over all possible tokenizations. |
| Outcome: | The proposed method improves on a range of LLM evaluation benchmarks for a variety of architectures, including transformers and state space models. |