Cutting LLM Evaluation Costs with SySRs: A Bandit Algorithm that Provably Exploits Model Similarity
arXiv:2606.07726v1 Announce Type: new Abstract: Large Language Models are typically benchmarked by evaluating every model on every test query. For practitioners seeking the best model to deploy, this is often wasteful: if a model clearly performs worse than others, there is no need to precisely estimate its performance. Best-arm identification algorithms can be naturally applied to drastically reduce costs by adaptively allocating evaluation budget. Further, language models often respond similarly to the same prompt-a property previous work has tried to leverage with mixed success. We propose
The rapid proliferation of Large Language Models and the increasing computational cost of their evaluation necessitate more efficient benchmarking methods now.
This development allows for more economical and faster evaluation of LLMs, accelerating research and development cycles and potentially reducing barriers to entry for new models.
Traditional exhaustive LLM evaluation methods will likely be supplemented or replaced by adaptive, cost-efficient algorithms, changing how models are benchmarked and compared.
- · AI researchers
- · LLM developers
- · Cloud computing providers (reduced egress/compute for evaluation)
- · Companies relying on brute-force evaluation
- · Inefficient LLM benchmarking services
Reduced costs and time for LLM development and deployment.
Faster iteration and potentially more diverse LLM architectures entering the market due to lower evaluation overhead.
The development of LLMs from smaller teams or startups becomes more feasible, potentially decentralizing AI innovation.
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Read at arXiv cs.LG