Papers by Xuantao Lu

2 papers
MoDification: Mixture of Depths Made Easy (2025.naacl-long)

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Challenge: Long-context efficiency is a trending topic in large language model (LLM) serving.
Approach: They propose a method to combine long-context efficiency and mixture of depths to bring down both latency and memory.
Outcome: The proposed method achieves 1.2 speedup in latency and 1.8 reduction in memory compared to original LLMs especially in long-context applications.
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning (2022.coling-1)

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Challenge: Existing methods to parse natural language into structured logical expressions have limitations due to paucity of labeled data.
Approach: They propose a scoring model to automatically learn a model-based reward . they also propose introducing a Chinese-PL/FOL dataset to compensate for paucity of labeled data .
Outcome: The proposed model outperforms competitors on several datasets.

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