Papers by Haoxiang Shi
An Empirical Study of Many-to-Many Summarization with Large Language Models (2025.acl-long)
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Jiaan Wang, Fandong Meng, Zengkui Sun, Yunlong Liang, Yuxuan Cao, Jiarong Xu, Haoxiang Shi, Jie Zhou
| Challenge: | Recent studies have shown that large language models (LLMs) have strong multilingual abilities, giving them the potential to perform M2MS in real applications. |
| Approach: | They propose to use many-to-many summarization (M2MS) to generate a brief summary in any language given a document also in any other language. |
| Outcome: | The proposed model outperforms zero-shot LLMs in terms of automatic evaluations. |
AlignCap: Aligning Speech Emotion Captioning to Human Preferences (2024.emnlp-main)
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| Challenge: | Existing methods for speech emotion capture often produce hallucinations and lose generalization on unseen speech. |
| Approach: | They propose to align speech emotion captioning to human preference based on large language model (LLM) and human preference regularization to eliminate factuality and faithfulness hallucinations. |
| Outcome: | Experiments show that AlignCap performs better than existing methods on Zero-shot SEC task. |
A Siamese CNN Architecture for Learning Chinese Sentence Similarity (2020.aacl-srw)
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| Challenge: | a deep neural architecture is used to learn a semantic similarity metric between two sentences . traditional methods of learning sentence similarity are based on the word level, which may not be sufficient. |
| Approach: | They propose a deep neural architecture which uses siamese convolutional neural network sharing model parameters to learn a semantic similarity metric between two sentences. |
| Outcome: | The proposed architecture outperforms baselines in similarity metrics for Chinese sentences by 8.7 points. |
LayerConnect: Hypernetwork-Assisted Inter-Layer Connector to Enhance Parameter Efficiency (2022.coling-1)
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| Challenge: | Existing parameter-efficient methods focus on reducing trainable parameters but neglect the inference speed, which limits the ability to deploy PLMs. |
| Approach: | They propose to use a hypernetwork-assisted inter-layer connector to enhance inference efficiency by tuning parameters inside a linear connector between two Transformer layers. |
| Outcome: | The proposed model reduces model parameters to 11.75% while preserving performance degradation to less than 5%. |