Papers by William Huang
NAP2: A Benchmark for Naturalness and Privacy-Preserving Text Rewriting by Learning from Human (2025.findings-emnlp)
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Shuo Huang, William Maclean, Xiaoxi Kang, Qiongkai Xu, Zhuang Li, Xingliang Yuan, Gholamreza Haffari, Lizhen Qu
| Challenge: | a large number of large language models are being used to protect user privacy . sanitizing sensitive text using two common strategies is the answer . |
| Approach: | They propose sanitizing sensitive text using deleting expressions and abstracting them . they propose a tool for text rewriting that uses crowdsourcing and large language models . |
| Outcome: | The proposed approach protects privacy before sending sensitive data to large language models . it combines crowdsourcing and large language modeling to create a text rewrite tool . |
Does Putting a Linguist in the Loop Improve NLU Data Collection? (2021.findings-emnlp)
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Alicia Parrish, William Huang, Omar Agha, Soo-Hwan Lee, Nikita Nangia, Alexia Warstadt, Karmanya Aggarwal, Emily Allaway, Tal Linzen, Samuel R. Bowman
| Challenge: | Many datasets for training and evaluating natural language understanding (NLU) models contain systematic artifacts that are identified only after data collection is complete. |
| Approach: | They propose to have linguists identify artifacts and gaps in the data and communicate with non-expert crowdworkers to adjust task instructions and incentives. |
| Outcome: | The proposed protocol does not increase accuracy on out-of-domain test sets, and adds a chatroom does not. |
Precise Task Formalization Matters in Winograd Schema Evaluations (2020.emnlp-main)
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| Challenge: | Recent results report a surge in performance to nearhuman levels on the Winograd Schema Challenge (WSC) however, variations in task formulation across papers and evaluations makes it hard to understand the true degree of recent progress. |
| Approach: | They propose to use a model with multiple choice to frame the task as multiple choice and reuse a pretrained language modeling head to mitigate the model's extreme sensitivity to hyperparameters. |
| Outcome: | The proposed frameworks improve the model's reasoning ability by framing the task as multiple choice and reuse of a pretrained language modeling head. |
Comparing Test Sets with Item Response Theory (2021.acl-long)
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Clara Vania, Phu Mon Htut, William Huang, Dhara Mungra, Richard Yuanzhe Pang, Jason Phang, Haokun Liu, Kyunghyun Cho, Samuel R. Bowman
| Challenge: | Recent results from large pretrained models show that many datasets are saturated and unlikely to detect further progress. |
| Approach: | They evaluate 29 datasets using predictions from 18 pretrained Transformer models on individual test examples. |
| Outcome: | The proposed datasets are saturated and unlikely to detect future improvements. |
Types of Out-of-Distribution Texts and How to Detect Them (2021.emnlp-main)
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| Challenge: | Current NLP models produce unreliable or catastrophic predictions when training and test distributions differ . current models tend to produce unreliability or even catastrophic predictions that hurt user trust. |
| Approach: | They categorize examples as exhibiting a background shift or semantic shift and use calibration and density estimation methods to detect OOD examples. |
| Outcome: | The proposed methods beat calibration methods in background shift settings and perform worse in semantic shift settings. |
Enhancing Factual Consistency of Abstractive Summarization (2021.naacl-main)
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| Challenge: | Abstractive summarization models often distort or fabricate facts in articles . factual inconsistency is a common problem with abstractive summaries . |
| Approach: | They propose a fact-aware summarization model FASum to extract factual relations into the summary generation process via graph attention. |
| Outcome: | The proposed model can produce abstractive summaries with higher factual consistency compared with existing systems and corrects factual errors via modifying only a few keywords. |
Tokenization Consistency Matters for Generative Models on Extractive NLP Tasks (2023.findings-emnlp)
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| Challenge: | Pretrained sequence-to-sequence (seq2sequ) models have been widely used to solve extractive tasks, where parts of the input are extracted to form the desired output. |
| Approach: | They propose a simple fix to tokenization inconsistency that damages extractive nature of generative models by causing performance drop and hallucination. |
| Outcome: | The proposed model performs better in both in-domain and out-of-domain datasets with a notable average of +1.7 F1 gain when a BART model is trained on SQuAD and evaluated on 8 QA datasets. |