Papers by Siyue Wu
AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression (2023.acl-long)
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| Challenge: | Existing knowledge distillation methods focus on the transfer of model-specific knowledge but overlook data-specific information. |
| Approach: | They propose an attribution-driven knowledge distillation approach which explores the token-level rationale behind the teacher model and transfers attribution knowledge to the student model. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark and shows that it is more efficient than existing methods. |
Rethinking Composed Image Retrieval Evaluation: A Fine-Grained Benchmark from Image Editing (2026.acl-long)
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Tingyu Song, Yanzhao Zhang, Mingxin Li, Zhuoning Guo, Dingkun Long, Pengjun Xie, Siyue Zhang, Yilun Zhao, Shu Wu
| Challenge: | Composed Image Retrieval (CIR) is a complex task in multimodal understanding . current CIR benchmarks lack a robust evaluation pipeline and limited query categories . |
| Approach: | They construct a fine-grained CIR benchmark that allows for precise control over modification types and content. |
| Outcome: | The proposed benchmark covers 5,000 high-quality queries structured across five main categories and fifteen subcategories. |
Directed Acyclic Graph Network for Conversational Emotion Recognition (2021.acl-long)
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| Challenge: | Empirical evidence shows that a good representation of conversation context significantly contributes to the model performance. |
| Approach: | They propose to encode query utterances with a directed acyclic graph to better model the intrinsic structure within a conversation. |
| Outcome: | The proposed model outperforms existing models on four ERC benchmarks with state-of-the-art models employed as baselines. |
Open-Domain Safety Policy Construction (2026.findings-eacl)
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| Challenge: | Moderation layers are core component of many products built on user-generated content. |
| Approach: | They propose a system that drafts a content moderation policy based on human-written seed domain information. |
| Outcome: | The proposed system outperforms definition-only and in-context learning baselines on openAI undesired content benchmarks and an in-house multimodal advertisement moderation benchmark. |
MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering (2025.findings-emnlp)
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| Challenge: | Existing methods for answering time-sensitive questions lack temporal reasoning . existing methods struggle with these time-intensive questions, authors say . |
| Approach: | They propose a temporal-based question-answering framework that integrates temporal perturbations and gold evidence labels into a question processing framework. |
| Outcome: | The proposed framework outperforms baseline retrieval methods in retrieval performance. |
MCC-KD: Multi-CoT Consistent Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable abilities in complex reasoning through chain of thought (CoT) prompting. |
| Approach: | They propose to generate multiple rationales for each question and enforce consistency among their predictions by minimizing the bidirectional KL-divergence between the answer distributions. |
| Outcome: | The proposed model achieves superior performance on in-distribution and commonsense reasoning benchmarks. |