Papers by Lijian Li

2 papers
From Charts to Code: A Hierarchical Benchmark for Multimodal Models (2026.acl-long)

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Challenge: Chart2Code is a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Approach: They introduce Chart2Code, a new benchmark for evaluating the natural language to chart code generation capabilities of large multimodal models.
Outcome: The proposed benchmark is the first to scale task complexity while capturing diverse scenarios.
Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)

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Challenge: Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously.
Approach: They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality.
Outcome: The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency.

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