
Developed and integrated the SLAKE medical visual question answering benchmark into the EvolvingLMMs-Lab/lmms-eval evaluation framework, enabling multilingual benchmarking with both English and Chinese task configurations. Designed a modular data processing and metrics utility module to support robust evaluation workflows, focusing on dataset filtering, normalization, and accuracy aggregation. Employed Python and HuggingFace Datasets to ensure scalable data engineering, and implemented comprehensive unit tests using a test-driven development approach. The work established a reusable baseline for future medical VQA experiments, expanding the framework’s evaluation coverage and reliability. Collaboration and configuration management were facilitated through YAML and git-based workflows throughout the project.
July 2026 - Key deliverables and impact for EvolvingLMMs-Lab/lmms-eval. Key features delivered: - SLAKE medical VQA benchmark integration into the evaluation framework, including English and Chinese task configurations. - Data processing and metrics utility module to support SLAKE evaluation. - Unit tests for dataset filtering, normalization, and accuracy aggregation. Major bugs fixed: - None reported this month; focus was on feature integration and test coverage. Overall impact and accomplishments: - Expanded evaluation coverage for medical VQA, enabling multilingual benchmarking and more reliable model comparisons. - Established a reusable baseline for future medical VQA experiments and benchmarking workflows. Technologies/skills demonstrated: - Python, modular evaluation design, data processing, unit testing (TDD), multilingual configuration management, and git-based collaboration (commit 0a75d6bab2ec6a5e402cecd153cb23b2b4b8ee71).
July 2026 - Key deliverables and impact for EvolvingLMMs-Lab/lmms-eval. Key features delivered: - SLAKE medical VQA benchmark integration into the evaluation framework, including English and Chinese task configurations. - Data processing and metrics utility module to support SLAKE evaluation. - Unit tests for dataset filtering, normalization, and accuracy aggregation. Major bugs fixed: - None reported this month; focus was on feature integration and test coverage. Overall impact and accomplishments: - Expanded evaluation coverage for medical VQA, enabling multilingual benchmarking and more reliable model comparisons. - Established a reusable baseline for future medical VQA experiments and benchmarking workflows. Technologies/skills demonstrated: - Python, modular evaluation design, data processing, unit testing (TDD), multilingual configuration management, and git-based collaboration (commit 0a75d6bab2ec6a5e402cecd153cb23b2b4b8ee71).

Overview of all repositories you've contributed to across your timeline