Papers by Junzhe Xu

2 papers
MC-MKE: A Fine-Grained Multimodal Knowledge Editing Benchmark Emphasizing Modality Consistency (2025.findings-acl)

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Challenge: Existing benchmarks for knowledge editing in multimodal large language models focus on limited scenarios due to the lack of rigorous definition of multimodal knowledge.
Approach: They propose a decomposed definition of multimodal knowledge and a benchmark to evaluate it.
Outcome: The proposed method reveals that it is difficult to define multimodal knowledge editing in LLMs.
PersonaTrace: Synthesizing Realistic Digital Footprints with LLM Agents (2026.eacl-industry)

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Challenge: Publicly available corpora cover only slivers of human activity, such as email threads, chat logs, purchase histories, sensor traces, and provide large-scale supervision for data-hungry machine-learning pipelines.
Approach: They propose a method for synthesizing realistic digital footprints using large language model agents from a structured user profile.
Outcome: The proposed method generates diverse sequences of user events, producing corresponding digital artifacts such as emails, messages, calendar entries, reminders, etc.

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