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Markovian Generation Chains in Large Language Models

arXiv:2603.11228v1 Announce Type: new Abstract: 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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Mingmeng Geng, Amr Mohamed, Guokan Shang, Michalis Vazirgiannis, Thierry Poibeau · · 1 min read · 17 views
#cs.CL #cs.AI #cs.LG

arXiv:2603.11228v1 Announce Type: new Abstract: 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.

Executive Summary

This article presents a novel framework for understanding the iterative inference process in large language models (LLMs), which the authors term Markovian generation chains. Through experiments and analysis, the authors demonstrate that LLMs can either converge to recurrent outputs or produce novel sentences, depending on factors such as the temperature parameter and initial input. The results offer insights into the dynamics of LLM inference and its implications for multi-agent systems. The study has significant implications for the development and deployment of LLMs in various applications, including natural language processing and machine learning. The findings also highlight the importance of carefully tuning the model parameters to achieve desired outcomes.

Key Points

  • ▸ The authors introduce the concept of Markovian generation chains to describe the iterative inference process in LLMs.
  • ▸ Experiments and analysis show that LLMs can either converge to recurrent outputs or produce novel sentences.
  • ▸ The results highlight the importance of model parameters, such as temperature and initial input, in determining the output of LLMs.

Merits

Strengths

The study provides a comprehensive framework for understanding the dynamics of LLM inference, which has significant implications for the development and deployment of LLMs in various applications.

Demerits

Limitations

The study focuses on a specific type of LLMs and may not be generalizable to other types of models or applications.

Expert Commentary

The study presents a novel framework for understanding the dynamics of LLM inference, which has significant implications for the development and deployment of LLMs in various applications. The findings highlight the importance of carefully tuning model parameters to achieve desired outcomes. However, the study's focus on a specific type of LLMs may limit its generalizability to other types of models or applications. Nevertheless, the study's results have significant implications for the development of policies and guidelines for the use of LLMs in various applications.

Recommendations

  • ✓ Future studies should investigate the generalizability of the study's findings to other types of LLMs and applications.
  • ✓ Researchers should carefully consider the model parameters and their impact on the output of LLMs in various applications.

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