Papers by Jacob Kahn
Reasoning over Public and Private Data in Retrieval-Based Systems (2023.tacl-1)
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| Challenge: | Existing retrieval systems assume relevant corpora are fully (e.g., publicly) accessible, but users are often unwilling to expose their private data to entities hosting public data. |
| Approach: | They propose a split iterative retrieval problem involving iterating retrieval over multiple privacy scopes and propose 'concurrentQA' benchmark to test this problem. |
| Outcome: | The proposed method improves on the existing retrieval methods but still suffers performance degradations when applied to a dataset from a public and private distribution. |
The Framework Tax: Disparities Between Inference Efficiency in NLP Research and Deployment (2023.emnlp-main)
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| Challenge: | Inference is estimated to make up 80 to 90% of ML cloud computing demand . |
| Approach: | They propose to identify bottlenecks in deep learning frameworks that are causing the disparity in model latency as hardware speed increases over time. |
| Outcome: | The proposed models show that the framework tax is increasing as the hardware speed increases over time. |
Altogether: Image Captioning via Re-aligning Alt-text (2024.emnlp-main)
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Hu Xu, Po-Yao Huang, Xiaoqing Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer
| Challenge: | Existing captioning models ignore existing alt-text metadata and lack transparency if training data is unknown. |
| Approach: | They propose an approach to edit and re-align alt-texts associated with images using human annotation. |
| Outcome: | The proposed approach improves image captions and improves text-to-image generation and zero-shot image classification tasks. |