EvolvingLMMs-Lab/lmms-eval
Jul 2025 – Jul 2025
1 Month active
Languages Used
PythonYAML
Technical Skills
Multilingual SupportNatural Language ProcessingPrompt Engineering
Jeongho Ju
PROFILE
Jeongho Ju
Worked on refining model instruction prompts within the lmms-eval repository, focusing on enhancing evaluation clarity and task adherence for multilingual support. Developed a targeted update to the Korean post-prompt, shifting the response format for multiple-choice questions to require selection by letter, which reduced ambiguity and improved consistency across tasks. Employed Python and YAML to implement and validate these changes, ensuring alignment with cross-task evaluation standards. The work emphasized prompt engineering and natural language processing, resulting in clearer evaluation signals and more reliable metrics. This feature laid the groundwork for future multilingual prompt enhancements while maintaining a strong focus on code quality.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
6
Activity Months1
Your Network
1212 peopleWork History
July 2025
1 Commits • 1 Features
Jul 1, 2025July 2025 objective: refine model instruction prompts to improve evaluation clarity and task adherence within the lmms-eval repository. Delivered a targeted post-prompt update for Korean prompts enabling multiple-choice responses, reducing ambiguity and improving consistency across tasks. No critical bugs fixed this month; focus remained on feature delivery and code quality. Impact: clearer evaluation signals, more reliable metrics, and a smoother path for future multilingual prompt enhancements.
1 Commits • 1 Features
Jul 1, 2025July 2025 objective: refine model instruction prompts to improve evaluation clarity and task adherence within the lmms-eval repository. Delivered a targeted post-prompt update for Korean prompts enabling multiple-choice responses, reducing ambiguity and improving consistency across tasks. No critical bugs fixed this month; focus remained on feature delivery and code quality. Impact: clearer evaluation signals, more reliable metrics, and a smoother path for future multilingual prompt enhancements.
July 2025
Activity
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Quality Metrics
Correctness100.0%
Maintainability100.0%
Architecture100.0%
Performance100.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonYAML
Technical Skills
Multilingual SupportNatural Language ProcessingPrompt Engineering
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