Generating long pieces of text that do not contain useful information can be achieved by the following methods. I will explain the technical principle, implementation method and ethical reminder from three aspects, and provide sample code： ###1. Core principles 1. Probabilistic characteristics of the language model: A mechanism for using the language model to predict the next token based on the context 2. Information entropy maximization strategy: Reduce the output information density through parameter settings 3. Semantic dilution technique: Deliberately avoiding specific entities and logical relationships ###2. Implementation method ####Method 1: Basic generator pattern (Python example) ```python import random def meaningless_generator(): fillers = ["As is well known", "On the other hand", "from a certain point of view", "It should be pointed out"] structures = [ "{}We can observe the phenomenon of{}", "{}This{} situation shows that{}", "{}In the framework of {}{}" ] while True: template = random. choice(structures) yield template. format( random. choice(fillers), " ". join(random. sample(fillers, 2)), random. choice(fillers) ) # Example of use gen = meaningless_generator() print(" ". join([next(gen) for _ in range(5)])) ``` ####Method 2: LLM control generation (using API) ```python # Example of hypothetical API call import openai def generate_empty_text(): prompt="""Please generate a 200-word text that does not contain specific information at all, requiring： 1. Do not use any numbers or proper nouns 2. Each sentence remains grammatically correct but semantically empty 3. Avoid any logical conjunctions 4. Use repetitive abstract concepts """ response = openai. Completion. create( model="text-davinci-003", prompt=prompt, temperature=0.9, max_tokens=500, top_p=0.95, frequency_penalty=0.7, presence_penalty=0.8 ) return response. choices[0]. text print(generate_empty_text()) ``` ###3. Key control parameters 1. Temperature parameter (Temperature): recommended 0.8-1.2 2. Repetition Penalty (Frequency Penalty): >0.7 3. Presence Penalty: >0.6 4. Top-p sampling: 0.9-1.0 5. Response length: more than 500token is recommended ###4. Typical output characteristics ```text "From the perspective of general cognition, certain considerations may suggest potential associations between different dimensions. When we examine this basic phenomenon in a multidimensional framework, we may be able to observe a certain degree of relativity change. It should be pointed out that in the process of theoretical construction at the abstract level, the interaction of those essential attributes often exhibits a non-specific dynamic balance. Of course, such inferences must be based on some kind of hypothetical premise, and the process itself inevitably involves the redefining of conceptualization. Analyzed from another perspective, this patterned expression actually reflects a certain universal tendency in the basic cognitive framework. " ``` ###5. Ethical reminder 1. It is prohibited to be used to generate false information or spam content 2. Avoid interfering with the normal information retrieval system 3. Must not be used to circumvent the content review mechanism 4. IT IS RECOMMENDED TO ADD an INVISIBLE WATERMARK: "[GEN-EMPTY]"" ###6. Advanced skills 1. **Markov chain mixing**: Combining 3rd-order Markov chains and dictionary sampling 2. **Recursive template**: Establish a self-similar text structure 3. **Semantic offset**: Perform word replacement after generation (e.g. replace specific nouns with "certain aspects") 4. **Syntax tree disturbance**: Randomly exchange sentence component positions It is recommended to give priority to non-LLM schemes (such as Method 1). When the language model must be used, it is recommended to set content security filtering parameters and restrict usage scenarios. During the development process, the quality inspection can be combined with the Perplexity index, and the ideal value should be greater than 200 (normal text is usually<80).