GPT as a Monte Carlo Language Tree: A Probabilistic Perspective

TL;DR

This paper introduces a probabilistic framework using Monte Carlo Language Trees to analyze GPT models, revealing their structural similarities, probabilistic reasoning nature, and insights into hallucination and bias in LLMs.

Contribution

It proposes representing language datasets and GPT models as Monte Carlo Language Trees, providing a new perspective for understanding LLMs' probabilistic pattern-matching behavior.

Findings

GPT models trained on the same dataset show structural similarity in GPT-Tree visualization.

Larger GPT models more closely approximate the Data-Tree.

Over 87% of GPT output tokens can be recalled by the Data-Tree.

Abstract

Large Language Models (LLMs), such as GPT, are considered to learn the latent distributions within large-scale web-crawl datasets and accomplish natural language processing (NLP) tasks by predicting the next token. However, this mechanism of latent distribution modeling lacks quantitative understanding and analysis. In this paper, we propose a novel perspective that any language dataset can be represented by a Monte Carlo Language Tree (abbreviated as ``Data-Tree''), where each node denotes a token, each edge denotes a token transition probability, and each sequence has a unique path. Any GPT-like language model can also be flattened into another Monte Carlo Language Tree (abbreviated as ``GPT-Tree''). Our experiments show that different GPT models trained on the same dataset exhibit significant structural similarity in GPT-Tree visualization, and larger models converge more closely to the Data-Tree. More than 87\% GPT output tokens can be recalled by Data-Tree. These findings may confirm that the reasoning process of LLMs is more likely to be probabilistic pattern-matching rather than formal reasoning, as each model inference seems to find a context pattern with maximum probability from the Data-Tree. Furthermore, we provide deeper insights into issues such as hallucination, Chain-of-Thought (CoT) reasoning, and token bias in LLMs.