Tencent/digitalhuman
Oct 2025 – Dec 2025
2 Months active
Languages Used
MarkdownPython
Technical Skills
DocumentationTechnical WritingAIAI DevelopmentData AnalysisLLM evaluation
Zhengliang Shi
PROFILE
Zhengliang Shi
Developed and documented the Social Welfare Function (SWF) Evaluation Framework for the Tencent/digitalhuman repository, focusing on LLM-based analysis of fairness and efficiency in task allocation. Leveraged Python and data analysis techniques to implement core framework components, including metrics for evaluating LLM agent welfare allocation. Enhanced onboarding and reproducibility by delivering comprehensive Markdown documentation, detailed environment setup instructions, and workflow visualizations. Structured updates improved maintainability and knowledge transfer for new contributors and stakeholders. The work provided researchers and product teams with a repeatable, auditable methodology for comparing LLMs on socially relevant metrics, accelerating experimentation and ensuring clarity in evaluation processes.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
21Total
Bugs
0
Commits
21
Features
3
Lines of code
2,333
Activity Months2
Your Network
8 peopleShared Repositories
8
Work History
December 2025
7 Commits • 2 Features
Dec 1, 2025December 2025 focused on delivering a runnable Social Welfare Function (SWF) Evaluation Framework for LLM-based analyses within Tencent/digitalhuman, complemented by comprehensive documentation to enable quick adoption and reproducibility. Key deliverables include a core SWF framework that supports task allocation, fairness metrics, and efficiency evaluations, along with extensive environment/setup guidance and usage steps. No major bugs were reported this month. Business value: provides researchers and product teams with a repeatable, auditable methodology to compare LLMs on socially-relevant metrics, accelerating experiments, ensuring fairness considerations, and reducing onboarding time through clear docs. Technologies demonstrated include LLM-driven evaluation workflow design, metrics for fairness and efficiency, and professional documentation practices with multiple README updates.
7 Commits • 2 Features
Dec 1, 2025December 2025 focused on delivering a runnable Social Welfare Function (SWF) Evaluation Framework for LLM-based analyses within Tencent/digitalhuman, complemented by comprehensive documentation to enable quick adoption and reproducibility. Key deliverables include a core SWF framework that supports task allocation, fairness metrics, and efficiency evaluations, along with extensive environment/setup guidance and usage steps. No major bugs were reported this month. Business value: provides researchers and product teams with a repeatable, auditable methodology to compare LLMs on socially-relevant metrics, accelerating experiments, ensuring fairness considerations, and reducing onboarding time through clear docs. Technologies demonstrated include LLM-driven evaluation workflow design, metrics for fairness and efficiency, and professional documentation practices with multiple README updates.
December 2025
October 2025
14 Commits • 1 Features
Oct 1, 2025October 2025 Monthly Summary for Tencent/digitalhuman: Focused on consolidating SWF Benchmark & Leaderboard documentation and assets, updating README/docs with explanations of fairness and efficiency metrics, findings on LLM agent welfare allocation, and formatting enhancements to improve readability and onboarding. No major defects fixed this month; primary impact was improved accessibility, maintainability, and discoverability of the SWF docs and assets for stakeholders and new contributors.
October 2025
14 Commits • 1 Features
Oct 1, 2025October 2025 Monthly Summary for Tencent/digitalhuman: Focused on consolidating SWF Benchmark & Leaderboard documentation and assets, updating README/docs with explanations of fairness and efficiency metrics, findings on LLM agent welfare allocation, and formatting enhancements to improve readability and onboarding. No major defects fixed this month; primary impact was improved accessibility, maintainability, and discoverability of the SWF docs and assets for stakeholders and new contributors.
Activity
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Quality Metrics
Correctness99.0%
Maintainability99.0%
Architecture100.0%
Performance99.0%
AI Usage26.6%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
AIAI DevelopmentData AnalysisDocumentationLLM evaluationMachine LearningPython ProgrammingTechnical Writingacademic writingcontent formattingdocumentationtechnical writing
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