ChatBot Arena | LLM Benchmark
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About Large Language Models (LLM)
Benchmarking Artificial Intelligences
Following ChatGPT’s significant success, numerous open-source large language models have emerged. These models are fine-tuned to adhere to instructions and can offer useful aid in answering users’ queries. Alpaca and Vicuna, which are based on LLaMA, and Google’s new model, Bard, are noteworthy examples.Even with the weekly introduction of new models, the community struggles to benchmark them efficiently. The task of benchmarking LLM assistants is notably difficult due to the open-ended nature of problems and the complexity of programming an automatic response quality evaluator. Consequently, we often rely on human evaluations through pairwise comparisons.
A good benchmark system based on pairwise comparison should possess certain characteristics.
br> Scalability: The system needs to accommodate a large number of models, especially when gathering ample data for all potential model pairs is impractical. Incrementality: The system should have the capacity to assess a new model with a minimal number of trials. Unique order: The system ought to establish a distinct ranking for all models. It should be clear which model ranks higher or if they are on par when comparing any two models.