Leaderboard Dilution: When Identical LLMs Dominate Top Rankings
A community post highlights how a single Large Language Model (LLM) or minor variations of it can occupy multiple top spots on open-source AI leaderboards under different names. This dilution occurs when users upload duplicate models or apply minor tweaks, skewing the rankings. This issue undermines the integrity of AI benchmarks, making it difficult for developers to identify truly unique and innovative models. It also raises questions about the effectiveness of current evaluation platforms like the Hugging Face Open LLM Leaderboard. The phenomenon is often driven by model merging techniques or simple renaming, which allow developers to submit slightly altered versions of high-performing models to climb the ranks. This practice clutters leaderboards and complicates independent model evaluation.
## BACKGROUND
Platforms like the Hugging Face Open LLM Leaderboard track and rank the performance of open-source LLMs across various benchmarks. However, techniques like model merging—which combines the weights of multiple LLMs using tools like mergekit without expensive retraining—have made it easy to generate derivative models that perform similarly to their parent models.