Papers by Robert Sim
Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe (2023.acl-long)
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Xiang Yue, Huseyin Inan, Xuechen Li, Girish Kumar, Julia McAnallen, Hoda Shajari, Huan Sun, David Levitan, Robert Sim
| Challenge: | Privacy concerns have increased in data-driven products due to the tendency of machine learning models to memorize sensitive training data. |
| Approach: | They propose a method for generating useful synthetic text with a formal privacy guarantee by fine-tuning a pretrained generative language model with DP. |
| Outcome: | The proposed method produces synthetic text competitive in terms of utility with its non-private counterpart, while providing strong protection against potential privacy leakages. |
Leveraging Structured Metadata for Improving Question Answering on the Web (2020.aacl-main)
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| Challenge: | Using metadata information from web pages can improve the performance of answer passage selection/reranking models. |
| Approach: | They propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy to learn the representation for metadata predicates in a hierarchical way. |
| Outcome: | The proposed model outperforms baseline models on the MS MARCO and Recipe-MARCO datasets and shows that it is more accurate than baseline models. |
Stereotyping Norwegian Salmon: An Inventory of Pitfalls in Fairness Benchmark Datasets (2021.acl-long)
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| Challenge: | Several recent efforts have focused on benchmark datasets consisting of pairs of contrastive sentences, which are often accompanied by metrics that aggregate an NLP system’s behavior on these pairs into measurements of harms. |
| Approach: | They apply a measurement modeling lens to inventory pitfalls that threaten benchmarks' validity as measurement models for stereotyping. |
| Outcome: | The proposed benchmarks lack clarity and assumptions that affect how they conceptualize and operationalize stereotyping. |
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis (2022.naacl-main)
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Fatemehsadat Mireshghallah, Vaishnavi Shrivastava, Milad Shokouhi, Taylor Berg-Kirkpatrick, Robert Sim, Dimitrios Dimitriadis
| Challenge: | Currently, global models are not able to produce personalized responses for individual users, based on their data. |
| Approach: | They propose a scheme for training a single shared model for all users by prepending a fixed, user-specific non-trainable string to each user’s input text. |
| Outcome: | The proposed method outperforms the state-of-the-art model on a suite of sentiment analysis datasets by up to 13 points. |
CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents (2026.acl-industry)
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Wenjie Fu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Lukas Wutschitz, Robert Sim, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a user’s behalf, also creates new risks for sensitive information leakage. |
| Approach: | They propose a Contextual Integrity-grounded benchmark that simulates enterprise workflows across five information-flow directions and evaluates whether agents can convey *essential* content while withholding *sensitive* context in dense retrieval settings. |
| Outcome: | The proposed model demonstrates that privacy failures are prevalent in enterprise workflows and that higher task utility correlates with increased privacy violations. |
Privacy Regularization: Joint Privacy-Utility Optimization in LanguageModels (2021.naacl-main)
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Fatemehsadat Mireshghallah, Huseyin Inan, Marcello Hasegawa, Victor Rühle, Taylor Berg-Kirkpatrick, Robert Sim
| Challenge: | Neural language models have a high capacity for memorization of training samples . however, this can cause privacy degradation and disparate impact on subgroups of users . |
| Approach: | They propose two privacy-preserving regularization methods for training language models that enable joint optimization of utility and privacy. |
| Outcome: | The proposed methods have favorable utility-privacy trade-off, faster training and uniform treatment of under-represented subgroups. |