Papers by Hojin Lee
Controlled Text Generation for Black-box Language Models via Score-based Progressive Editor (2024.acl-long)
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| Challenge: | Existing methods to control text generation are inapplicable to black-box models or suffer a trade-off between control and fluency. |
| Approach: | They propose a new approach to control text generation that modifies context at the token level during the generation process of a backbone language model and guides subsequent text to naturally include the target attributes. |
| Outcome: | The proposed method can regulate the attributes of the generated text while utilizing the capability of the backbone large language models. |
PROM: Pivoted and Regulated Optimization for Multilingual Instruction Learning (2025.naacl-short)
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| Challenge: | Existing solutions to large language models (LLMs) are English-centric, hindering their application to 6500+ existing languages. |
| Approach: | They propose to append English tuning data with its translated pair to solve this problem . they identify English as an internal pivot language and propose to regulate between them . |
| Outcome: | The proposed model is able to generalize on multiple benchmarks across different languages. |
Normalizing Mutual Information for Robust Adaptive Training for Translation (2022.emnlp-main)
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| Challenge: | Neural machine translation models have been reported to generate hallucinations . despite the success of the models, there are still challenges to improve fluency . |
| Approach: | They propose a scoring metric for the importance of target sentences and tokens to encourage fluent translations. |
| Outcome: | The proposed metric improves translation fluency and source-faithfulness . the proposed nmi model is not properly normalized, the authors argue . |
Consistency is Key: On Data-Efficient Modality Transfer in Speech Translation (2023.findings-emnlp)
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| Challenge: | End-to-end approaches to speech translation suffer from data scarcity compared to machine translation (MT). |
| Approach: | They propose a method which combines knowledge distillation and consistency learning to break the dilemma of learning-forgetting. |
| Outcome: | The proposed method outperforms the previous methods on a MuST-C dataset even without additional data. |