To develop more objective and standardized evaluation frameworks for LLM-generated text, particularly in decision-critical applications such as finance, we can adopt a multi-faceted approach that incorporates both quantitative and qualitative metrics. First, we should establish clear criteria for evaluation that align with the specific needs of the domain. For instance, in finance, metrics could include accuracy, relevance, and clarity of the generated text, as well as its ability to influence decision-making effectively.
Utilizing automated scoring systems that assess grammatical correctness, coherence, and logical flow can provide a baseline for objective evaluation. However, given the subjective nature of text interpretation, it is crucial to complement these automated metrics with human evaluations. This can be achieved by creating standardized rubrics that define what constitutes high-quality text in various dimensions, such as convincingness, usefulness, and logical coherence.
Furthermore, incorporating feedback loops where both amateur and expert users assess the generated text can help refine the evaluation process. By analyzing user reactions and decision changes, as highlighted in the study, we can better understand the impact of LLM-generated content on real-world decision-making. This dual approach of combining objective metrics with user feedback will lead to a more comprehensive and standardized evaluation framework that can be adapted across different domains.