Papers by Taewhoo Lee
Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models (2025.acl-long)
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| Challenge: | Quantization is a practical solution for deploying Large Language Models in resource-constrained environments. |
| Approach: | They propose an outlier-safe pre-training approach that prevents outlier formation . they validate a 1.4B-parameter model on 1 trillion tokens with no outliers . |
| Outcome: | The proposed model achieves a 35.7 average score on 1 trillion tokens with 2% training overhead. |
ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage (2025.naacl-long)
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| Challenge: | Existing evaluation methods do not assess whether large language models fully utilize contextual information. |
| Approach: | They introduce a new metric to assess LLMs' ability to fully utilize contextual information. |
| Outcome: | The proposed benchmark comprises 1,986 test instances spanning four long-context tasks with high IC scores in the domains of books, debates, medicine, and law. |
CompAct: Compressing Retrieved Documents Actively for Question Answering (2024.emnlp-main)
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| Challenge: | Existing methods to condense extensive documents with no loss of information are difficult to implement in real-world scenarios. |
| Approach: | They propose a framework that employs an active strategy to condense extensive documents without losing key information. |
| Outcome: | The proposed framework improves performance and compression rate on multi-hop question-answering benchmarks. |
DICE-BENCH: Evaluating the Tool-Use Capabilities of Large Language Models in Multi-Round, Multi-Party Dialogues (2025.findings-acl)
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| Challenge: | Existing function-calling benchmarks focus on single-turn interactions but ignore complexity of real-world scenarios. |
| Approach: | They propose a framework that constructs practical function-calling datasets by synthesizing conversations through a tool graph that maintains dependencies across rounds. |
| Outcome: | The proposed framework synthesizes conversations through a tool graph that maintains dependencies across rounds and a multi-agent system with distinct personas to enhance dialogue naturalness. |