研究大模型反复生成文本时的演化规律，发现输出可能收敛或持续创新。
Markovian Generation Chains in Large Language Models
- 将多轮生成建模为无记忆的马尔可夫链，每步仅依赖前一句输出。
- 温度参数和初始句子决定输出多样性：可增可减，非单调变化。
- 适用于理解多智能体系统中生成行为的长期动态，适合研究生成稳定性者。
大语言模型（LLM）的广泛应用引发一个重要问题：当文本被反复通过LLM处理时，其演化过程如何？本文将这一迭代推理过程定义为马尔可夫生成链，每一步以特定提示模板和前一输出作为输入，不包含任何历史记忆。在迭代改写和往返翻译实验中，输出要么收敛到一个有限的重复集合，要么在有限范围内持续生成新句子。通过句级马尔可夫链建模与模拟数据分析，我们发现迭代过程可增加或减少句子多样性，具体取决于温度参数和初始输入句。这些结果为迭代式LLM推理的动力学特性及其在多智能体系统中的影响提供了重要洞见。
原文摘要 · Abstract (English)
The widespread use of large language models (LLMs) raises an important question: how do texts evolve when they are repeatedly processed by LLMs? In this paper, we define this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior memory. In iterative rephrasing and round-trip translation experiments, the output either converges to a small recurrent set or continues to produce novel sentences over a finite horizon. Through sentence-level Markov chain modeling and analysis of simulated data, we show that iterative process can either increase or reduce sentence diversity depending on factors such as the temperature parameter and the initial input sentence. These results offer valuable insights into the dynamics of iterative LLM inference and their implications for multi-agent LLM systems.
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