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SWE-bench Multilingual leaderboard shows questionable scores and submission origins

By PulseAugur Editorial · [1 sources] ·

The SWE-bench Multilingual leaderboard on Hugging Face, intended to evaluate LLM performance on coding tasks across 300 real GitHub issues in nine languages, has inconsistencies. Many listed scores, including the top two, do not appear to be valid percentages derived from the 300 tasks, suggesting they may be averages or use different denominators. Furthermore, a significant portion of the submissions originate from open pull requests, with many opened by only a few accounts, raising questions about the verification and independence of the results. AI

IMPACT Raises concerns about the reliability of LLM coding benchmark results and the integrity of leaderboards.

RANK_REASON Analysis of a benchmark's leaderboard and data integrity. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

SWE-bench Multilingual leaderboard shows questionable scores and submission origins

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39 / 100
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Analysis of a benchmark's leaderboard and data integrity. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Ward Ed ·

    SWE-bench Multilingual on Hugging Face: 15 of 34 Scores Are Not a Count Out of 300 Tasks

    <h2> TL;DR </h2> <ul> <li>On November 9, 2026 the Hugging Face SWE-bench Multilingual board lists <strong>34 rows from 34 repos, none verified.</strong> 30 rows are read from a pull request that is still open (all 30 opened by one of two accounts) and 4 from a repo's main branch.…

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