Papers by Donglin Di

2 papers
Towards Multi-System Log Anomaly Detection (2025.acl-industry)

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Challenge: Existing models require dataset-specific training, causing costly procedures and performance bottlenecks.
Approach: They propose a log anomaly detection model with semantic relational reasoning that extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors.
Outcome: The proposed model extracts cross-system semantic patterns and encodes them as high-dimensional learnable vectors.
CodeIF: Benchmarking the Instruction-Following Capabilities of Large Language Models for Code Generation (2025.acl-industry)

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Challenge: CodeIF assesses the ability of large language models to adhere to task-oriented instructions in code generation tasks.
Approach: They introduce a benchmark designed to assess LLMs' ability to adhere to task-oriented instructions within diverse code generation scenarios.
Outcome: The proposed benchmark assesses LLMs' ability to adhere to task-oriented instructions in code generation tasks across a wide range of complexity levels and programming domains.

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