Papers by Chenxu Wu

3 papers
A General Knowledge Injection Framework for ICD Coding (2025.findings-acl)

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Challenge: Existing methods to improve ICD coding focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other.
Approach: They propose a general knowledge injection framework that integrates three key types of knowledge without specialized design of additional modules.
Outcome: The proposed framework outperforms baseline models and is comparable to models relying on extra human annotations.
Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)

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Challenge: Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity.
Approach: They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states.
Outcome: The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters.
Task-Oriented Clustering for Dialogues (2021.findings-emnlp)

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Challenge: Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them.
Approach: They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations .
Outcome: The proposed model outperforms baselines on three public datasets on all metrics.

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