How to Evaluate Request Optimization of Language Model with Chess

While the capabilities of large language models (LLM) develop rapidly, measuring them is becoming increasingly difficult. Traditional criteria are saturated, open test sets risk data leakage and require expensive infrastructure to evaluate complex tasks. These problems in the field of optimisation (APO) are more aggravated because they need to be repeated evaluations throughout the search process.

New research released in arXiv suggests the chess game as a measure to overcome these challenges:

-Low cost: Chess positions can be automatically evaluated, no need human observer or complex infrastructure

• Reliable testing compliance: Thousands of times can be operated during automatic prompt improvement research
• Objectif metrics: It can be clearly determined if the game wins and is lost, there is no mugle evaluation
• Natural difficulty scale: Chess positions can vary from simple problems to very complex work
• No risk of contamination: Reliable results are taken because the teaching data of language models are not already contaminated with chess positions

This approach leads an interesting way to test the ability of language models strategic thinking and decision making. The researchers are developing a methodology that uses chess to measure how effective the istem optimization techniques are. In this way, APO algorithms can develop cheaper and quickly.

This study brings a new perspective to AI assessment culture and opens the door of more efficient criteria.