Papers by Myungjin Lee
Enhancing Large Language Models through Transforming Reasoning Problems into Classification Tasks (2024.lrec-main)
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Tarun Raheja, Raunak Sinha, Advit Deepak, Will Healy, Jayanth Srinivasa, Myungjin Lee, Ramana Kompella
| Challenge: | Existing approaches to improve LLMs' reasoning capabilities for constraint satisfaction problems (CSPs) are needed to solve complex tasks. |
| Approach: | They propose a method that leverages the LLM's ability to decide when to call a function from a set of logical-linguistic primitives, each of which can interact with a local “scratchpad” memory and logical inference engine. |
| Outcome: | The proposed method improves the reasoning capabilities of large language models for constraint satisfaction problems by 40% over baselines. |
StitchLLM: Serving LLMs, One Block at a Time (2025.acl-long)
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Bodun Hu, Shuozhe Li, Saurabh Agarwal, Myungjin Lee, Akshay Jajoo, Jiamin Li, Le Xu, Geon-Woo Kim, Donghyun Kim, Hong Xu, Amy Zhang, Aditya Akella
| Challenge: | Existing techniques like distillation and pruning are not efficient for large language models. |
| Approach: | They propose a dynamic model routing framework that uses a powerful bottom model to process all queries and a lightweight routing mechanism to allocate computational resources appropriately. |
| Outcome: | The proposed framework improves system throughput while minimizing performance degradation. |