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
Price refined model instruction prompts within the lmms-eval repository to enhance evaluation clarity and task adherence for Korean multiple-choice questions. Focusing on prompt engineering and multilingual support, Price updated the post-prompt instructions to require selection of a lettered option, replacing less specific guidance and reducing ambiguity in model responses. The work, implemented in Python and YAML, standardized the instruction format and improved consistency across tasks, resulting in clearer evaluation signals and more reliable metrics. Although no bugs were addressed during this period, Price’s targeted feature delivery laid the groundwork for future multilingual prompt enhancements and strengthened cross-task evaluation standards.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
1Total
Bugs
0
Commits
1
Features
1
Lines of code
6
Activity Months1
Your Network
1077 people
Work 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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