TRUCE: Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs

Understand

Benchmarking is the de-facto standard for evaluating LLMs, due to its speed, replicability and low cost.

• However, recent work has pointed out that the majority of the open source benchmarks available today have been contaminated or leaked into LLMs, meaning that LLMs have access to test data during pretraining and/or fine-tuning.
• This raises serious concerns about the validity of benchmarking studies conducted so far and the future of evaluation using benchmarks.
• To solve this problem, we propose Private Benchmarking, a solution where test datasets are kept private and models are evaluated without revealing the test data to the model.