LLM-based natural language generation (NLG) evaluation has become a critical area in AI research. Traditional metrics like BLEU and ROUGE, which rely on n-gram overlap, are insufficient due to their limited correlation with human judgments. Recent advances in large language models (LLMs) have led to new evaluation methods, including LLM-derived metrics, prompting LLMs, fine-tuning LLMs, and human-LLM collaboration. These methods aim to improve the accuracy and efficiency of NLG evaluation. LLM-derived metrics, such as BERTScore and BARTScore, use embeddings or generation probabilities to assess text quality. Prompting LLMs involves using designed prompts to evaluate text, while fine-tuning LLMs uses labeled data to enhance their evaluation capabilities. Human-LLM collaboration leverages the strengths of both to achieve more nuanced evaluations. LLM-based evaluation methods have shown promising results, but they face challenges such as robustness, efficiency, and fairness. For example, LLM-derived metrics may lack robustness in different scenarios, require more computational resources, and may introduce social bias. Prompting LLMs can be biased due to position effects and may favor longer responses. Fine-tuning LLMs can be computationally expensive and may inherit biases from training data. Future research directions include developing unified benchmarks for LLM-based NLG evaluation, expanding evaluation to low-resource languages and new tasks, and exploring diverse forms of human-LLM collaboration. These efforts aim to improve the accuracy, efficiency, and fairness of NLG evaluation, ensuring that LLMs can effectively assess text quality in various contexts.LLM-based natural language generation (NLG) evaluation has become a critical area in AI research. Traditional metrics like BLEU and ROUGE, which rely on n-gram overlap, are insufficient due to their limited correlation with human judgments. Recent advances in large language models (LLMs) have led to new evaluation methods, including LLM-derived metrics, prompting LLMs, fine-tuning LLMs, and human-LLM collaboration. These methods aim to improve the accuracy and efficiency of NLG evaluation. LLM-derived metrics, such as BERTScore and BARTScore, use embeddings or generation probabilities to assess text quality. Prompting LLMs involves using designed prompts to evaluate text, while fine-tuning LLMs uses labeled data to enhance their evaluation capabilities. Human-LLM collaboration leverages the strengths of both to achieve more nuanced evaluations. LLM-based evaluation methods have shown promising results, but they face challenges such as robustness, efficiency, and fairness. For example, LLM-derived metrics may lack robustness in different scenarios, require more computational resources, and may introduce social bias. Prompting LLMs can be biased due to position effects and may favor longer responses. Fine-tuning LLMs can be computationally expensive and may inherit biases from training data. Future research directions include developing unified benchmarks for LLM-based NLG evaluation, expanding evaluation to low-resource languages and new tasks, and exploring diverse forms of human-LLM collaboration. These efforts aim to improve the accuracy, efficiency, and fairness of NLG evaluation, ensuring that LLMs can effectively assess text quality in various contexts.