Can large language models (LLMs) fairly evaluate the quality of text? A new research paper, "DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation," tackles this critical question. As LLMs become increasingly popular for automated text evaluation, their reliability as judges comes under scrutiny. Traditional methods rely on single-prompt LLM evaluations, comparing the output to human judgments. However, this approach lacks transparency and may not accurately reflect the LLM's evaluation capabilities. DnA-Eval introduces a two-stage process inspired by educational rubrics: decomposition and aggregation. First, the LLM identifies key evaluation aspects or uses predefined criteria. Then, it scores responses pairwise for each aspect and assigns weights based on their importance. An external calculator aggregates these weighted scores to produce a final judgment. Experiments show DnA-Eval significantly improves LLM evaluation performance across various benchmarks, outperforming direct scoring and chain-of-thought prompting methods. The research also reveals fascinating insights into how LLMs generate evaluation aspects and assign weights, offering a deeper understanding of their strengths and weaknesses. While DnA-Eval demonstrates promising results, challenges remain. The computational cost is higher than simpler methods, and the optimal number of evaluation aspects may vary depending on the task. Furthermore, relying solely on human preference labels as the gold standard for evaluation may not always be ideal. Future research could explore dynamic aspect generation and alternative evaluation metrics. Despite these challenges, DnA-Eval represents a significant step towards more transparent, reliable, and interpretable LLM-based text evaluation, paving the way for more sophisticated AI judges in the future.