Workshop on Trustworthy NLP 2023: all accepted papers
- Can we trust the evaluation on ChatGPT?Rachith Aiyappa, Jisun An, Haewoon Kwak et al.68 citations · our summary
- Privacy- and Utility-Preserving NLP with Anonymized data: A case study of Pseudonymization ↗Oleksandr Yermilov, Vipul Raheja, Artem Chernodub30 citations
- Detecting Personal Information in Training Corpora: an Analysis ↗Nishant Subramani, Sasha Luccioni, Jesse Dodge et al.20 citations
- Training Data Extraction From Pre-trained Language Models: A Survey ↗Shotaro Ishihara19 citations
- Automated Ableism: An Exploration of Explicit Disability Biases in Sentiment and Toxicity Analysis Models ↗Pranav Narayanan Venkit, Mukund Srinath, Shomir Wilson15 citations
- Distinguishing Fact from Fiction: A Benchmark Dataset for Identifying Machine-Generated Scientific Papers in the LLM Era. ↗Edoardo Mosca, Mohamed Hesham Ibrahim Abdalla, Paolo Basso et al.14 citations
- Improving Factuality of Abstractive Summarization via Contrastive Reward Learning ↗I-chun Chern, Zhiruo Wang, Sanjan Das et al.11 citations
- Strength in Numbers: Estimating Confidence of Large Language Models by Prompt Agreement ↗Gwenyth Portillo Wightman, Alexandra Delucia, Mark Dredze10 citations
- Reliability Check: An Analysis of GPT-3's Response to Sensitive Topics and Prompt Wording ↗Aisha Khatun, Daniel Brown9 citations
- GPTs Don't Keep Secrets: Searching for Backdoor Watermark Triggers in Autoregressive Language Models ↗Evan Lucas, Timothy Havens7 citations
- Guiding Text-to-Text Privatization by Syntax ↗Stefan Arnold, Dilara Yesilbas, Sven Weinzierl6 citations
- IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks ↗Xuanli He, Jun Wang, Benjamin Rubinstein et al.6 citations
- Expanding Scope: Adapting English Adversarial Attacks to Chinese ↗Hanyu Liu, Chengyuan Cai, Yanjun Qi5 citations
- Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script ↗Xi Cao, Dolma Dawa, Nuo Qun et al.5 citations
- A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on TwitterKyra Yee, Alice Schoenauer Sebag, Olivia Redfield et al.4 citations · our summary
- Towards Faithful Explanations for Text Classification with Robustness Improvement and Explanation Guided Training ↗Dongfang Li, Baotian Hu, Qingcai Chen et al.4 citations
- An Empirical Study of Metrics to Measure Representational Harms in Pre-Trained Language ModelsSaghar Hosseini, Hamid Palangi, Ahmed Hassan Awadallah3 citations · our summary
- DEPTH+: An Enhanced Depth Metric for Wikipedia Corpora Quality ↗Saied Alshahrani, Norah Alshahrani, Jeanna Matthews3 citations
- Make Text Unlearnable: Exploiting Effective Patterns to Protect Personal Data ↗Xinzhe Li, Ming Liu3 citations
- Driving Context into Text-to-Text Privatization ↗Stefan Arnold, Dilara Yesilbas, Sven Weinzierl2 citations
- Enabling Classifiers to Make Judgements Explicitly Aligned with Human Values ↗Yejin Bang, Tiezheng Yu, Andrea Madotto et al.2 citations
- Enhancing textual counterfactual explanation intelligibility through Counterfactual Feature Importance ↗Milan Bhan, Jean-noel Vittaut, Nicolas Chesneau et al.2 citations
- On The Real-world Performance of Machine Translation: Exploring Social Media Post-authors' Perspectives ↗Ananya Gupta, Jae Takeuchi, Bart Knijnenburg2 citations
- Are fairness metric scores enough to assess discrimination biases in machine learning? ↗Fanny Jourdan, Laurent Risser, Jean-michel Loubes et al.1 citation
- Debunking Biases in Attention ↗Shijing Chen, Usman Naseem, Imran Razzak1 citation
- Examining the Causal Impact of First Names on Language Models: The Case of Social Commonsense Reasoning ↗Sullam Jeoung, Jana Diesner, Halil Kilicoglu1 citation
- Linguistic Properties of Truthful Response ↗Bruce W. Lee, Benedict Florance Arockiaraj, Helen Jin1 citation
- Sample Attackability in Natural Language Adversarial Attacks ↗Vyas Raina, Mark Gales1 citation
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