Papers by Wenxiang Hu
DeTiME: Diffusion-Enhanced Topic Modeling using Encoder-decoder based LLM (2023.findings-emnlp)
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| Challenge: | Neural Topic Models and Large Language Models (LLMs) primarily use contextual embeddings from LLMs, which are not optimal for clustering or topic generation. |
| Approach: | They propose a framework that leverages Encoder-Decoders to generate highly clusterable embeddings that could generate topics that exhibit enhanced clusterability and enhanced semantic coherence compared to existing methods. |
| Outcome: | The proposed framework is efficient to train and exhibits high adaptability, demonstrating its potential for a wide array of applications. |
WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning (2024.acl-long)
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| Challenge: | Recent work shows that Code Large Language Models can address a wide range of code-related tasks. |
| Approach: | They propose a method to generate widespread and versatile instruction data from open source code datasets and use it to train code-related models. |
| Outcome: | The proposed model outperforms open-source models in generalization ability across code-related tasks. |