Papers by Hanlin Zhu
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented Dialog (D19-1)
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| Challenge: | Existing methods to learn dialog policy require elaborate design and user goals. |
| Approach: | They propose an algorithm that estimates the reward signal and infers the user goal in dialog sessions. |
| Outcome: | The proposed algorithm achieves higher task success than state-of-the-art models on a multi-domain task-oriented dialog dataset. |
Learning Personalized Alignment for Evaluating Open-ended Text Generation (2024.emnlp-main)
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| Challenge: | Traditional evaluation metrics rely heavily on lexical similarity with human-written references, showing poor correlation with human judgments and failing to account for alignment with the diversity of human preferences. |
| Approach: | They propose an interpretable evaluation framework that evaluates alignment with specific human preferences by providing detailed comments and fine-grained scoring. |
| Outcome: | The proposed framework outperforms GPT-4 in Kendall correlation and accuracy with zero-shot reviewers. |
What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)
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| Challenge: | Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context. |
| Approach: | They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering. |
| Outcome: | The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset. |
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs (2023.emnlp-main)
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| Challenge: | Recent work has shown that large language models are superior to conventional methods in various tasks. |
| Approach: | They propose a data-independent quantization algorithm that leaves outliers in the weight and quantization ranges . they find the algorithm runs over 10 times faster than the data-dependent methods . |
| Outcome: | The proposed method runs over 10 times faster than the data-dependent methods. |