Frequency penalty is a mechanism that penalizes frequent occurrences of new vocabulary in the generated text, reducing the likelihood of the model repeating the same words. The higher the value, the more likely it is to reduce repeated words.

• 2.0 When the morning news starts playing, I noticed that my TV now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now now (The most frequent word is "now" with a percentage of 44.79%)
• 1.0 He always watches the news in the morning, watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching watching (The most frequent word is "watching" with a percentage of 57.69%)
• 0.0 When the morning sun shines into the small restaurant, a tired mailman appears at the door, holding a bag of mail in his hand. The owner warmly prepares breakfast for him, and he starts sorting the mail while enjoying his breakfast. (The most frequent word is "the" with a percentage of 8.45%)
• 1.0 A deep sleep girl is awakened by a warm sunbeam. She sees the first ray of sunlight in the morning, surrounded by the sounds of birds and the fragrance of flowers, everything is full of vitality. (The most frequent word is "the" with a percentage of 5.45%)
• 2.0 Every morning, he sits on the balcony to have breakfast. In the gentle sunset, everything looks very peaceful. However, one day, as he was about to pick up his breakfast, an optimistic little bird flew by, bringing him a good mood for the day. (The most frequent word is "the" with a percentage of 4.94%)

Note:

It is worth noting that the presence penalty parameter, along with other parameters such as temperature and top-p, collectively affect the quality of the generated text. Compared to other parameters, the presence penalty parameter focuses more on the originality and repetitiveness of the text, while the temperature and top-p parameters have a greater impact on the randomness and determinism of the generated text. By adjusting these parameters properly, comprehensive control of the quality of the generated text can be achieved.