Papers by Eric Smith
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)
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Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, Cassidy Trier, Aaron Sarnat, Jenna James, Jon Borchardt, Bailey Kuehl, Evie Yu-Yen Cheng, Karen Farley, Taira Anderson, David Albright, Carissa Schoenick, Luca Soldaini, Dirk Groeneveld, Rock Yuren Pang, Pang Wei Koh, Noah A. Smith, Sophie Lebrecht, Yejin Choi, Hannaneh Hajishirzi, Ali Farhadi, Jesse Dodge
| Challenge: | tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting . |
| Approach: | They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches . |
| Outcome: | The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models . |
Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models (2020.acl-main)
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| Challenge: | a dataset of imagined and recalled stories is used to study the cognitive processes involved in storytelling, contrasting imagination and recollection of events. |
| Approach: | They use a dataset of 7,000 stories to study the cognitive processes involved in storytelling, contrasting imagination and recollection of events. |
| Outcome: | The proposed measures show that imagined stories have a substantially more linear narrative flow compared to recalled stories in which adjacent sentences are more disconnected. |
Multilingual Holistic Bias: Extending Descriptors and Patterns to Unveil Demographic Biases in Languages at Scale (2023.emnlp-main)
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Marta Costa-jussà, Pierre Andrews, Eric Smith, Prangthip Hansanti, Christophe Ropers, Elahe Kalbassi, Cynthia Gao, Daniel Licht, Carleigh Wood
| Challenge: | Multilingual HolisticBias dataset includes 20,459 sentences in 50 languages . dataset is intended to uncover demographic imbalances and quantify mitigations . |
| Approach: | They propose a multilingual extension of the HolisticBias dataset . they use 118 demographic descriptors and three patterns to build multilingual sentences . |
| Outcome: | The proposed model improves translation quality when the source input only differs in gender . it also improves when the masculine human reference is used in the model . |
Evaluating Models’ Local Decision Boundaries via Contrast Sets (2020.findings-emnlp)
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Matt Gardner, Yoav Artzi, Victoria Basmov, Jonathan Berant, Ben Bogin, Sihao Chen, Pradeep Dasigi, Dheeru Dua, Yanai Elazar, Ananth Gottumukkala, Nitish Gupta, Hannaneh Hajishirzi, Gabriel Ilharco, Daniel Khashabi, Kevin Lin, Jiangming Liu, Nelson F. Liu, Phoebe Mulcaire, Qiang Ning, Sameer Singh, Noah A. Smith, Sanjay Subramanian, Reut Tsarfaty, Eric Wallace, Ally Zhang, Ben Zhou
| Challenge: | Standard test sets for supervised learning evaluate in-distribution generalization but are misleading when a dataset has systematic gaps. |
| Approach: | They propose a more rigorous annotation paradigm for NLP that helps to close systematic gaps in the test data. |
| Outcome: | The proposed model performs significantly lower on contrast sets than on the original test sets—up to 25% in some cases. |
Toxicity in Multilingual Machine Translation at Scale (2023.findings-emnlp)
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Marta Costa-jussà, Eric Smith, Christophe Ropers, Daniel Licht, Jean Maillard, Javier Ferrando, Carlos Escolano
| Challenge: | In this paper, we evaluate and analyze added toxicity when translating a large dataset from English into 164 languages. |
| Approach: | They evaluate added toxicity when translating a large dataset from English into 164 languages. |
| Outcome: | The results show that added toxicity is more prevalent in low-resource languages than in high-resolution translations. |
ROBBIE: Robust Bias Evaluation of Large Generative Language Models (2023.emnlp-main)
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David Esiobu, Xiaoqing Tan, Saghar Hosseini, Megan Ung, Yuchen Zhang, Jude Fernandes, Jane Dwivedi-Yu, Eleonora Presani, Adina Williams, Eric Smith
| Challenge: | generative large language models (LLMs) are becoming more performant and prevalent . we need tools to measure and improve their fairness, authors say . |
| Approach: | They propose to compare 6 different prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models. |
| Outcome: | The proposed model can be tested on more datasets to better characterize and mitigate biases . the study compared 6 prompt-based bias and toxicity metrics across 12 demographic axes and 5 families of generative large language models. |