Analyzing constrained LLM through PDFA-learning: Analyzing large language models

Table of Links

Abstract and 1 Introduction

2 Language models

3 Learning algorithm

4 Analyzing large language models

5 Conclusions. Acknowledgements, and References

A. Proof of Proposition 2.1

B. Proof of Proposition 2.2

C. Proof of Proposition 2.3

D. Proof of Proposition 2.4

4 Analyzing large language models

Guiding generation Guiding an LLM to generate strings of interest consists in synchronizing it with a automaton that defines the set of symbols that can be drawn at each step of the generation process, which could be constrained further by a sampling strategy. To illustrate how the synchronization works, consider the language model given by the PDFA L in Fig. 4 (0-probabilities are omitted). The guide G is a weighted automaton that defines a mask at each state: a weight of 1 for a symbol means it is allowed, otherwise it is not. L × G is a weighted automaton whose underlying structure is the product automaton, and weights are obtained by taking the product of the distribution of the state of L with the weights of the state of G. To obtain PDFA B, we apply the sampling strategy samptop2.

[2] https://huggingface.co/docs/transformers/main_classes/tokenizer