Breaking Down EvalGen: Who Validates the Validators?

The evaluation of large language models (LLMs) is crucial to ensure their reliability and effectiveness in various applications. However, the process of evaluating LLMs can be challenging due to the subjective nature of some criteria and the need for human judgement. In this paper review, we discuss a study that explores the use of LLMs as judges for evaluating other LLMs. The study presents a framework called EvalGen, which aims to improve evaluation metrics by incorporating human feedback and iteratively refining evaluation criteria. The EvalGen framework consists of four main steps: pretest, grading, customization, and implementation. In the pretest step, users define their evaluation criteria and create an initial set of examples with labels. The LLM judge then evaluates these examples based on the defined criteria. In the grading step, human evaluators grade the LLM's performance on the same set of examples to identify any misalignments between the LLM's judgement and human expectations. The customization step involves adjusting evaluation criteria based on feedback from human evaluators. This can include adding or removing criteria, modifying existing criteria, or changing their weightage. The final implementation step incorporates the refined evaluation criteria into the LLM application for continuous monitoring and improvement. One key takeaway from this study is the importance of iterative evaluation and refinement of evaluation criteria to ensure accurate and reliable results. Additionally, the use of golden data sets can help users better understand their evaluation criteria and identify any misalignments between human judgement and LLM performance. While there is some skepticism around using LLMs as judges for evaluating other LLMs, particularly in production environments, this study demonstrates that with proper customization and iteration, LLMs can be effective tools for evaluating LLM applications.