Papers by Yiran Zhong
CDEvalSumm: An Empirical Study of Cross-Dataset Evaluation for Neural Summarization Systems (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing evaluation methods for text summarization systems are limited to in-domain setting, where supervised pre-trained models are evaluated on the same dataset. |
| Approach: | They propose to use a cross-dataset evaluation approach to evaluate different summarization systems in a multi-domain setting. |
| Outcome: | The proposed model can be used to evaluate text summarization systems on different datasets. |
The Devil in Linear Transformer (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing linear transformers suffer from performance degradations on various tasks and corpus. |
| Approach: | They propose a new linear attention that replaces scaling with a normalization to stabilize gradients and confine attention to neighbouring tokens in early layers. |
| Outcome: | The proposed model outperforms vanilla transformers on the long-range arena benchmark while being significantly more space-time efficient. |
Extractive Summarization as Text Matching (2020.acl-main)
Copied to clipboard
| Challenge: | Currently, most of the neural extractive summarization systems score and extract sentences individually and model the relationship between sentences. |
| Approach: | They propose to instantiate a neural extractive summarization task as a semantic text matching problem and use it to match a source document and candidate summaries in a semantic space. |
| Outcome: | The proposed framework is faster and more efficient than existing frameworks. |
Accelerating Toeplitz Neural Network with Constant-time Inference Complexity (2023.emnlp-main)
Copied to clipboard
| Challenge: | Toeplitz Neural Networks outperform commonly used Transformer-based models while benefiting from log-linear space-time complexities. |
| Approach: | They propose to convert TNNs to SSMs during inference to combine strengths of TNN and SSM approaches. |
| Outcome: | The proposed method outperforms most Transformer-based models while retaining the advantage of constant inference complexity. |
Scaling Laws for Linear Complexity Language Models (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing scaling laws for large language models are unclear, but they are useful for scalability. |
| Approach: | They propose scaling laws for linear complexity language models to establish a foundation for their scalability. |
| Outcome: | The proposed models demonstrate superior linguistic proficiency and knowledge retention. |
ReAugKD: Retrieval-Augmented Knowledge Distillation For Pre-trained Language Models (2023.acl-short)
Copied to clipboard
Jianyi Zhang, Aashiq Muhamed, Aditya Anantharaman, Guoyin Wang, Changyou Chen, Kai Zhong, Qingjun Cui, Yi Xu, Belinda Zeng, Trishul Chilimbi, Yiran Chen
| Challenge: | Knowledge distillation (KD) is an effective compression technique to derive a smaller student model from a larger teacher model by transferring the knowledge embedded in the teacher's network. |
| Approach: | They propose a framework and loss function that preserves the semantic similarities of teacher and student training examples to enable the student to retrieve from the knowledge base effectively. |
| Outcome: | The proposed framework preserves the semantic similarities of teacher and student training examples to achieve state-of-the-art performance on the GLUE benchmark. |