Papers by Taesung Lee
Matching Pairs: Attributing Fine-Tuned Models to their Pre-Trained Large Language Models (2023.acl-long)
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| Challenge: | generative large language models (LLMs) are widely used but fine-tuned to improve performance on downstream applications leads to violations of model licenses, model theft, and copyright infringement. |
| Approach: | They propose to trace back the origin of a model trained to its pre-trained base model . they use different knowledge levels and attribution strategies to find out how the model was trained . |
| Outcome: | The proposed method can trace back 8 out of 10 fine tuned models with different knowledge levels and attribution strategies. |
Supervising Unsupervised Open Information Extraction Models (D19-1)
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| Challenge: | Existing supervised methods that use labeled training data are expensive and difficult to adapt to new domains. |
| Approach: | They propose a supervised open information extraction framework that leverages unsupervised Open IE systems and labeled data to improve system performance. |
| Outcome: | The proposed method outperforms existing supervised and unsupervised models by a significant margin. |
Full-Stack Information Extraction System for Cybersecurity Intelligence (2022.emnlp-industry)
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| Challenge: | a rapid increase in cyberattacks and attacks pose enormous challenges to security analysts. |
| Approach: | They propose a full-stack information extraction system for the cybersecurity domain that extracts 26 entity types, 20 fixed relations and the temporal information of the relations. |
| Outcome: | The proposed system can extract 26 entity types, 20 fixed rela and temporal information of relations. |