Papers by Xianquan Wang
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. |
I-AM-G: Interest Augmented Multimodal Generator for Item Personalization (2024.emnlp-main)
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| Challenge: | e-commerce and recommender systems lack a framework for personalized generation . a new framework extracts tags from multimodal information of items that the user has interacted with . |
| Approach: | They propose a framework that extracts tags from multimodal information and rewrites item description . they then use a decoupled text-to-text and image-to image retriever to search for similar item text . |
| Outcome: | The proposed framework can generate results aligned with user preferences . it can be used in e-commerce and recommender systems to win over diverse user base . |
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. |
Dynamic Multi-granularity Attribution Network for Aspect-based Sentiment Analysis (2024.emnlp-main)
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| Challenge: | Existing methods for predicting sentiment polarity of aspects are susceptible to interference caused by irrelevant contexts and lack sentiment knowledge at a data-specific level. |
| Approach: | They propose a novel Aspect-based sentiment analysis method that leverages attention scores to model the relationships between aspects and contexts. |
| Outcome: | The proposed method is able to predict sentiments from a set of five benchmark datasets. |