aiqbench analysis
The withdrawal of this paper highlights a critical tension in LLM evaluation: while open benchmarks like HELM and BIG-Bench ensure reproducibility, their static nature invites data leakage that renders leaderboard rankings unreliable. The study's core findings—that high scores on public test sets do not guarantee real-world utility and that dynamic or private benchmarks are necessary—remain relevant despite the manuscript's removal due to overlap with prior work. For the tech community, this reinforces the need to move beyond static, publicly accessible datasets to maintain the integrity of model assessment.
Pitfalls of Evaluating Language Models with Open Benchmarks
arXiv:2507.00460v3 Announce Type: replace Abstract: Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support comparative analysis, reproducibility, and systematic progress tracking in Language Model (LM) research. Yet, this openness also creates substantial risks of data leakage during LM testing--deliberate or inadvertent, thereby undermining the fairness and reliability of leaderboard rankings and leaving them vulnerable to manipulation by unscrupulous actors. We illustrate the severity of this issue by intentionally constructing cheating models: smaller variants of BART, T5, and GPT-2, fine-tuned directly on publicly available test-sets. As expected, these models excel on the target benchmarks but fail terribly to generalize to comparable unseen testing sets. We then examine task specific simple paraphrase-based safeguarding strategies to mitigate the impact of data leakage and evaluate their effectiveness and limitations. Our findings underscore three key points: (i) high leaderboard performance on limited open, static benchmarks may not reflect real-world utility; (ii) private or dynamically generated benchmarks should complement open benchmarks to maintain evaluation integrity; and (iii) a reexamination of current benchmarking practices is essential for reliable and trustworthy LM assessment.
This paper has been withdrawn by Md. Najib Hasan
Abstract:Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support comparative analysis, reproducibility, and systematic progress tracking in Language Model (LM) research. Yet, this openness also creates substantial risks of data leakage during LM testing--deliberate or inadvertent, thereby undermining the fairness and reliability of leaderboard rankings and leaving them vulnerable to manipulation by unscrupulous actors. We illustrate the severity of this issue by intentionally constructing cheating models: smaller variants of BART, T5, and GPT-2, fine-tuned directly on publicly available test-sets. As expected, these models excel on the target benchmarks but fail terribly to generalize to comparable unseen testing sets. We then examine task specific simple paraphrase-based safeguarding strategies to mitigate the impact of data leakage and evaluate their effectiveness and limitations. Our findings underscore three key points: (i) high leaderboard performance on limited open, static benchmarks may not reflect real-world utility; (ii) private or dynamically generated benchmarks should complement open benchmarks to maintain evaluation integrity; and (iii) a reexamination of current benchmarking practices is essential for reliable and trustworthy LM assessment.
After further review, we found that the core contribution and methodology substantially overlap with previously published work. As a result, the manuscript does not provide a sufficiently distinct or original contribution in its current form. To avoid repetition in the literature and prevent possible confusion for readers, we believe withdrawal is the most appropriate action
Computation and Language (cs.CL)
arXiv:2507.00460 [cs.CL]
(or arXiv:2507.00460v3 [cs.CL] for this version)
From: Md. Najib Hasan [view email] [v1] Tue, 1 Jul 2025 06:17:48 UTC (104 KB) [v2] Wed, 7 Jan 2026 04:35:13 UTC (9,555 KB) [v3] Wed, 3 Jun 2026 23:11:39 UTC (1 KB) (withdrawn)
Full text under the publisher's open licence · 290 words
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