## 3 Results
### Classification of Evaluation Metrics for AIGC Models
#### 3.4.2 Evaluation Metrics for NLP Tasks  
_F1 Score:_ It is a measure of a test's accuracy that considers both precision (the number of correct positive results divided by the number of all positive results) and recall (the number of correct positive results divided by the number of positive results that should have been returned). The F1 score is the harmonic mean of precision and recall. The formula is F1 = 2 * (precision * recall)/(precision + recall). The T5 Model [39] used the F1 score as one of the evaluation metrics for the translation tasks. _BLEU (Bilingual Evaluation Understudy):_ This is a metric for evaluating the similarity between a generated sentence and a reference sentence. A perfect match yields a score of 1.0. It is widely used not only in machine translation tasks but also in text generation applications. Various language translation and image captioning AI models, including T5 [39], Conv52S [132], Seq2 Seq [108], MusCaps [48], \"Show and Tell: Neural Image Captioning\" [59], WT5 [121] and RTT-GAN [62], have incorporated BLEU as one of their key evaluation metrics. _Recall:_ Also known as sensitivity or true positive rate (TPR), it is the fraction of the total amount of relevant instances that were actually retrieved. It can be thought of as the ability of a model to find all the relevant cases within a dataset. For image captioning tasks, generative AI models like MusCaps [48] use recall as one of their evaluation metrics. _METEOR (Metric for Evaluation of Translation with Explicit Ordering):_ It is an evaluation technique designed specifically for machine translation. This method calculates the harmonic mean of unigram precision and recall, where recall carries more weight than precision. What sets METEOR apart from other evaluation metrics is its inclusion of additional features like stemming and synonym matching in addition to the traditional exact word matching. Conversational AI models like GPT-3 and T5 [112] and image captioning models like MusCaps [48], GTR-LSTM [129], and Show and Tell: Neural Image Captioning [59] have used METEOR as their evaluation metrics. _CIDEr (Consensus-based Image Description Evaluation):_ It is a metric designed to evaluate the quality of image descriptions generated by models like RTT-GAN [62] and MusCaps [48] that capture the consensus among human references. _Median Rank:_ The median rank is a frequently employed metric in retrieval tasks. In such tasks, a system receives a query and retrieves a list of items ranked according to their relevance. The rank of the first item that is deemed relevant is recorded, and the median of these ranks across a collection of queries is then calculated to determine the Median Rank. _EM-Diff (Exact Match Difference):_ EM-Diff is an evaluation metric commonly used in conversational AI tasks, particularly in question-answering systems. It measures the difference between the exact match scores of two systems on a given dataset. The exact match score represents the proportion of responses that exactly match the ground-truth answers. EM-Diff provides insights into the improvement or degradation of the exact match performance between different models. Conversational AI models like PEER [34] use EM-Diff as one of their evaluation metrics. _BLEURT (Bilingual Evaluation Understudy for Natural Language Understanding in Translation):_ BLEURT is an evaluation metric specifically designed for assessing the quality of machine translation systems, question-answering models like InstructGPT [111]. It measures the similarity between human-generated reference translations and machine-generated translations. BLEURT is trained using a combination of deep learning techniques and is capable of providing a continuous score that represents the quality of translations. A higher BLEURT score indicates better translation quality. _Root Mean Squared Relative Error (RMSRE):_ RMSRE is an evaluation metric used to measure the accuracy of predictions or forecasts for models like GAN-FD [156].