EvolvingLMMs-Lab/lmms-eval
May 2026 – Jul 2026
2 Months active
Languages Used
Python
Technical Skills
Machine LearningNatural Language ProcessingPythonConfiguration ManagementData AnalysisError Handling
Taesung Kim
PROFILE
Taesung Kim
Contributed to the EvolvingLMMs-Lab/lmms-eval repository by enhancing document-to-text extraction for vision-only scenarios and improving evaluation reliability. Developed a Python-based pipeline that automates accurate extraction of questions and options from image-based documents, reducing manual intervention and accelerating feedback cycles for vision-language models. Introduced anonymous access to the VisualWebBench dataset by updating YAML configurations, enabling evaluation runs without authentication tokens. Addressed error handling by guarding against empty metric buckets, returning NaN for missing data to prevent crashes across multiple evaluation utilities. Demonstrated skills in configuration management, data analysis, and natural language processing while collaborating on code reviews and cross-functional improvements.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
3Total
Bugs
1
Commits
3
Features
2
Lines of code
52
Activity Months2
Your Network
96 peopleSame Organization
@upstage.ai1
Work History
July 2026
2 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for EvolvingLMMs-Lab/lmms-eval: Delivered key reliability improvements and features that reduce user friction and stabilize evaluation pipelines. Implemented anonymous access to the VisualWebBench dataset by updating seven subtask YAML configurations to set dataset token requirement to false, enabling runs without a Hugging Face authentication token. Fixed a critical evaluation crash by guarding empty metric buckets, returning NaN when no samples exist, applicable across groundingme, refcoco, refcoco+, and refcocog utilities. These changes enhance CI reliability, enable broader testing, and improve end-to-end evaluation throughput in token-free environments.
2 Commits • 1 Features
Jul 1, 2026July 2026 monthly summary for EvolvingLMMs-Lab/lmms-eval: Delivered key reliability improvements and features that reduce user friction and stabilize evaluation pipelines. Implemented anonymous access to the VisualWebBench dataset by updating seven subtask YAML configurations to set dataset token requirement to false, enabling runs without a Hugging Face authentication token. Fixed a critical evaluation crash by guarding empty metric buckets, returning NaN when no samples exist, applicable across groundingme, refcoco, refcoco+, and refcocog utilities. These changes enhance CI reliability, enable broader testing, and improve end-to-end evaluation throughput in token-free environments.
July 2026
May 2026
1 Commits • 1 Features
May 1, 2026Month: 2026-05 — Delivered a targeted enhancement to the Vision-Only Document-to-Text (Doc2Text) pipeline in lmms-eval, along with a critical fix to improve extraction accuracy from image-based documents. This work strengthens the reliability of downstream evaluation workflows by automating more accurate extraction of questions and answer options from visuals, reducing manual correction and enabling faster iteration on vision-language models. Key achievements in this month include delivering the Vision-Only Document-to-Text Enhancement with Post-Prompt and rectifying the doc_to_text flow to use post_prompt, resulting in more robust extraction in vision-only scenarios.
May 2026
1 Commits • 1 Features
May 1, 2026Month: 2026-05 — Delivered a targeted enhancement to the Vision-Only Document-to-Text (Doc2Text) pipeline in lmms-eval, along with a critical fix to improve extraction accuracy from image-based documents. This work strengthens the reliability of downstream evaluation workflows by automating more accurate extraction of questions and answer options from visuals, reducing manual correction and enabling faster iteration on vision-language models. Key achievements in this month include delivering the Vision-Only Document-to-Text Enhancement with Post-Prompt and rectifying the doc_to_text flow to use post_prompt, resulting in more robust extraction in vision-only scenarios.
Activity
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Quality Metrics
Correctness93.4%
Maintainability93.4%
Architecture80.0%
Performance80.0%
AI Usage66.6%
Skills & Technologies
Programming Languages
Python
Technical Skills
Configuration ManagementData AnalysisError HandlingHugging Face HubMachine LearningNatural Language ProcessingPythonYAML
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