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
During July 2025, Price Oh refined model instruction prompts within the EvolvingLMMs-Lab/lmms-eval repository, focusing on enhancing evaluation clarity and task adherence for Korean multiple-choice questions. By updating the post-prompt instructions to require selection of a lettered option, Price reduced ambiguity and standardized response formats, which improved the reliability of evaluation metrics. The work leveraged Python and YAML, applying prompt engineering and multilingual support skills to align with evolving cross-task standards. Although no bugs were addressed during this period, the targeted feature delivery demonstrated depth in natural language processing and laid a foundation for future multilingual prompt enhancements.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
6
Activity Months1
Your Network
1143 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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