thunlp/SIR-Bench
Dec 2024 – Jan 2025
2 Months active
Languages Used
MarkdownPython
Technical Skills
Configuration ManagementData AnalysisData EvaluationLLM EvaluationMachine LearningNatural Language Processing
Alexander Lam
PROFILE
Alexander Lam
Developed and integrated a Bradley-Terry subjective evaluation framework within the thunlp/SIR-Bench repository, enabling robust pairwise model comparisons across datasets such as AlpacaEval, CompassArena, WildBench, and Arena Hard. Leveraged Python and statistical modeling to implement scalable evaluation workflows, including new configuration files for both single-turn and multi-turn scenarios. Enhanced benchmarking fidelity by introducing features like predicted win rate reporting and a toggle between win rates and Elo ratings, supporting more actionable model selection. The work emphasized configuration management, data analysis, and reproducibility, resulting in a data-driven evaluation process that aligns benchmarking with business needs and reduces ambiguity in competitive tasks.
Overall Statistics
Feature vs Bugs
100%Features
Repository Contributions
4Total
Bugs
0
Commits
4
Features
2
Lines of code
4,134
Activity Months2
Your Network
17 peopleSame Organization
@pjlab.org.cn2
Work History
January 2025
2 Commits • 1 Features
Jan 1, 2025January 2025: Delivered Bradley-Terry subjective evaluation integration for the Arena Hard dataset within SIR-Bench, including new configuration files and the ability to report predicted win rates versus a baseline model in evaluation reports, with a toggle to switch between predicted win rates and Elo ratings. This work enhances benchmarking fidelity, enabling more actionable insights for model selection and data-driven deployment decisions. It also improves reproducibility and aligns evaluation with business goals, reducing ambiguity in competitive tasks.
2 Commits • 1 Features
Jan 1, 2025January 2025: Delivered Bradley-Terry subjective evaluation integration for the Arena Hard dataset within SIR-Bench, including new configuration files and the ability to report predicted win rates versus a baseline model in evaluation reports, with a toggle to switch between predicted win rates and Elo ratings. This work enhances benchmarking fidelity, enabling more actionable insights for model selection and data-driven deployment decisions. It also improves reproducibility and aligns evaluation with business goals, reducing ambiguity in competitive tasks.
January 2025
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 – thunlp/SIR-Bench: Delivered a Bradley-Terry subjective evaluation framework enabling pairwise model comparisons across AlpacaEval, CompassArena, and WildBench. Introduced CompassArena-SubjectiveBench dataset, plus single-turn and multi-turn evaluation configurations and a detailed results summarizer. This work provides scalable, data-driven evaluation capabilities that accelerate benchmarking cycles and support product decisions.
December 2024
2 Commits • 1 Features
Dec 1, 2024December 2024 – thunlp/SIR-Bench: Delivered a Bradley-Terry subjective evaluation framework enabling pairwise model comparisons across AlpacaEval, CompassArena, and WildBench. Introduced CompassArena-SubjectiveBench dataset, plus single-turn and multi-turn evaluation configurations and a detailed results summarizer. This work provides scalable, data-driven evaluation capabilities that accelerate benchmarking cycles and support product decisions.
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Quality Metrics
Correctness97.6%
Maintainability85.0%
Architecture97.6%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
MarkdownPython
Technical Skills
Configuration ManagementData AnalysisData EvaluationLLM EvaluationMachine LearningMachine Learning EvaluationNatural Language ProcessingPythonSoftware DevelopmentStatistical Modeling
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