Google Research recently introduced a method termed Batch Calibration (BC) aimed at enhancing the performance of Large Language Models (LLMs) by reducing sensitivity to design decisions like template choice. This method is poised to address performance degradation issues and foster robust LLM applications by mitigating biases associated with template selections, label spaces, and demonstration examples. The unveiling took place on October 13, 2023, and the method was elucidated by Han Zhou, a Student Researcher, and Subhrajit Roy, a Senior Research Scientist at Google Research.
The Challenge
The performance of LLMs, particularly in in-context learning (ICL) scenarios, has been found to be significantly influenced by the design choices made during their development. The prediction outcomes of LLMs can be biased due to these design decisions, which could result in unexpected performance degradation. Existing calibration methods have…
