Papers by Gibbeum Lee
Prompted LLMs as Chatbot Modules for Long Open-domain Conversation (2023.findings-acl)
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| Challenge: | Using pre-trained large language models (LLMs) as individual modules for long-term consistency and flexibility is a challenge for open-domain chatbots due to the computational burden of updating models with billions of parameters and the scarcity of data in the dialogue domain. |
| Approach: | They propose a method that uses pre-trained large language models as individual modules for long-term consistency and flexibility. |
| Outcome: | The proposed method is on par with fine-tuned chatbot models in open-domain conversations, showing it can create consistent and engaging chatbots. |
Accelerating Multilingual Language Model for Excessively Tokenized Languages (2024.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have shown a significant degree of multilingual proficiency on a variety of tasks in multiple languages. |
| Approach: | They propose a framework to fine-tune a language model head and fine-track it while preserving its performance. |
| Outcome: | The proposed framework increases the generation speed by 1.7 while maintaining the performance of pre-trained multilingual models on target monolingual tasks. |