Papers by Lulu Xu
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation (2023.acl-long)
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| Challenge: | Existing controllable dialogue generation models focus on single attribute and lack generalization capability to out-of-distribution multiple attribute combinations. |
| Approach: | They propose a compositional generalization model that learns from seen attributes and generalizes to unseen combinations. |
| Outcome: | The proposed model can learn from seen attribute values and generalize to unseen combinations. |
MAFMO: Multi-modal Adaptive Fusion with Meta-template Optimization for Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing approaches focus on single-modality adjustments, leading to suboptimal alignment and limited generalization. |
| Approach: | They propose a plug-and-play framework for visual recognition that integrates a Harmonic Cross-Modal Adapter and a Meta-Template Optimization module. |
| Outcome: | Extensive experiments across multiple fine-grained visual recognition benchmarks show that MAFMO consistently improves existing methods’ performance on both novel classes and harmonic mean while maintaining robustness under various challenging conditions with minimal computational overhead. |
PSC: Extending Context Window of Large Language Models via Phase Shift Calibration (2024.emnlp-main)
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| Challenge: | Large-scale language models (LLMs) have shown impressive results across a variety of tasks. |
| Approach: | They propose a module for calibrating the frequencies predefined by existing methods . they conducted extensive experiments across multiple models and tasks . |
| Outcome: | The proposed method reduces perplexity as the context window size is varied from 16k to 32k and up to 64k. |
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization (2022.naacl-main)
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| Challenge: | Existing methods for domain adaptation of abstractive dialogue summarization lack generalization ability on new domains. |
| Approach: | They propose a domain-oriented prefix-tuning model that uses a prefix module to alleviate domain entanglement and discrete prompts to guide the model to focus on key contents of dialogues. |
| Outcome: | The proposed model can be generalized to two multi-domain dialogue summarization datasets. |
Revisit Overconfidence for OOD Detection: Reassigned Contrastive Learning with Adaptive Class-dependent Threshold (2022.naacl-main)
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Yanan Wu, Keqing He, Yuanmeng Yan, QiXiang Gao, Zhiyuan Zeng, Fujia Zheng, Lulu Zhao, Huixing Jiang, Wei Wu, Weiran Xu
| Challenge: | Existing methods for OOD detection are based on labeled in-domain data . detecting out-of-domain (OOD) or unknown intents is challenging . |
| Approach: | They propose a novel reassigned contrastive learning method to discriminate IND intents for over-confident OOD and an adaptive class-dependent local threshold mechanism to separate similar IND and OOD intents. |
| Outcome: | The proposed method is effective for both aspects of overconfidence issues. |
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization (2021.findings-emnlp)
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| Challenge: | Abstractive dialogue summarization suffers from a lot of factual errors due to scattered salient elements in multi-speaker information interaction process. |
| Approach: | They propose a slot-driven beam search algorithm to give priority to generating salient elements in a limited length by "filling-in-the-blanks". |
| Outcome: | The proposed algorithm improves the slot-driven beam search algorithm on different types of factual errors and human evaluation further verifies the results. |
Improving Abstractive Dialogue Summarization with Graph Structures and Topic Words (2020.coling-main)
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| Challenge: | Existing approaches to summarize textual information are hard to capture long-distance relationships. |
| Approach: | They propose a Topic-word Guided Dialogue Graph Attention network to model the dialogue as an interaction graph according to topic word information. |
| Outcome: | The proposed model outperforms baseline models on two corpus corpus models. |