Papers by Zhaocheng Du
CaPEdit: Capability-Preserving Lifelong Knowledge Editing For Language Models (2026.findings-acl)
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| Challenge: | Existing approaches to incrementally correct factual inaccuracies in large language models (LLMs) but sequential edits can lead to substantial degradation of capabilities. |
| Approach: | They propose a framework that preserves model capabilities under LKE by decoupling fast-updating factual knowledge from slow-evolving procedural knowledge. |
| Outcome: | The proposed framework improves capability preservation across all fundamental capabilities by 49.78% and achieves superior editing performance. |
Bridging Relevance and Reasoning: Rationale Distillation in Retrieval-Augmented Generation (2025.findings-acl)
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Pengyue Jia, Derong Xu, Xiaopeng Li, Zhaocheng Du, Xiangyang Li, Yichao Wang, Yuhao Wang, Qidong Liu, Maolin Wang, Huifeng Guo, Ruiming Tang, Xiangyu Zhao
| Challenge: | Existing approaches to rerank and align documents based on reasoning capabilities of large language models (LLMs) . prior work shows that LLMs have exceptional reasoning and text generation capabilities . |
| Approach: | They propose a rationale extraction method that leverages reasoning capabilities of large language models to extract the rationales necessary for answering a query. |
| Outcome: | The proposed method is compared with baseline methods on two tasks across three datasets. |
From ID to LLM: Rethinking Representation Learning for Recommendation (2026.acl-long)
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| Challenge: | Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations as they capture behavioral and semantic spaces respectively. |
| Approach: | They propose a Profile-then-Embedding framework for recommendation that integrates semantic user and item profiles and a Personalized Embedded stage to encode these profiles into task-aligned recommendation embeddings. |
| Outcome: | The proposed framework achieves significant gains across three benchmark datasets, including cold-start and long-tail scenarios. |
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment (2025.emnlp-main)
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Zhipeng Bian, Jieming Zhu, Qijiong Liu, Wang Lin, Guohao Cai, Zhaocheng Du, Jiacheng Sun, Zhou Zhao, Zhenhua Dong
| Challenge: | Large language models and diffusion models have opened new possibilities for AI-generated content . personalized cover image generation remains underexplored despite its critical role in boosting user engagement on digital platforms. |
| Approach: | They propose a framework that integrates MLLM-based prompting with personalized preference alignment to generate high-quality, contextually relevant covers. |
| Outcome: | The proposed framework improves image quality, semantic fidelity, and personalization, leading to stronger user appeal and offline recommendation accuracy in downstream tasks. |
Q-PRM: Adaptive Query Rewriting for Retrieval-Augmented Generation via Step-level Process Supervision (2025.findings-emnlp)
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| Challenge: | Existing approaches to rewriting queries often lack supervision signals for intermediate steps . existing approaches rely on outcome-supervised training or heuristic rules to guide the rewrite process . |
| Approach: | They propose a query rewriting framework that generates process-level supervision signals for intermediate steps. |
| Outcome: | a new query rewriting framework outperforms existing approaches on open-domain QA benchmarks. |