DEP: A Decentralized Large Language Model Evaluation Protocol
With the rapid development of Large Language Models (LLMs), a large number of benchmarks have been proposed. However, most benchmarks lack unified evaluation standard and require the manual implementation of custom scripts, making results hard to ensure consistency and reproducibility. Furthermore, mainstream evaluation frameworks are centralized, with datasets and answers, which increases the risk of benchmark leakage. To address these issues, we propose a Decentralized Evaluation Protocol (DEP), a decentralized yet unified and standardized evaluation framework through a matching server without constraining benchmarks. The server can be mounted locally or deployed remotely, and once adap...
| Authors | Jianxiang Peng, Junhao Li, Hongxiang Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 1 March 2026 |
| Last updated at source | 1 March 2026 |
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