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Language Model Maps for Prompt-Response Distributions via Log-Likelihood Vectors

arXiv:2603.18593v1 Announce Type: new Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In this space, distances between models approximate the KL divergence between the corresponding conditional distributions. Experiments on a large collection of publicly available language models show that the maps capture meaningful global structure, including relationships to model attributes and task performance. The method also captures systematic shifts induced by prompt modifications and their approximate additive compositionality, suggesting a way to analyze and predict the effects of composite prompt operations. We further introduce pointwise mutual information (PMI) vectors to reduce the influence of unconditional distributions; in some cases, PMI-based model maps better reflect training-data-related differences. Overall, the framework supports the analys

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Yusuke Takase, Momose Oyama, Hidetoshi Shimodaira · · 1 min read · 8 views
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arXiv:2603.18593v1 Announce Type: new Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing their conditional distributions. In this space, distances between models approximate the KL divergence between the corresponding conditional distributions. Experiments on a large collection of publicly available language models show that the maps capture meaningful global structure, including relationships to model attributes and task performance. The method also captures systematic shifts induced by prompt modifications and their approximate additive compositionality, suggesting a way to analyze and predict the effects of composite prompt operations. We further introduce pointwise mutual information (PMI) vectors to reduce the influence of unconditional distributions; in some cases, PMI-based model maps better reflect training-data-related differences. Overall, the framework supports the analysis of input-dependent model behavior.

Executive Summary

This article proposes a novel method for representing language models by log-likelihood vectors over prompt-response pairs. The method constructs model maps to compare conditional distributions, with distances approximating KL divergence. Experiments demonstrate the maps capture meaningful global structure and task performance relationships, as well as prompt modification effects. The framework supports analysis of input-dependent model behavior. Pointwise mutual information vectors are introduced to reduce unconditional distribution influence. The approach's potential for predicting composite prompt operations and analyzing training-data-related differences is significant.

Key Points

  • ▸ The method represents language models by log-likelihood vectors over prompt-response pairs
  • ▸ Model maps constructed for comparing conditional distributions approximate KL divergence
  • ▸ Experiments show maps capture global structure and task performance relationships
  • ▸ Pointwise mutual information vectors introduced to reduce unconditional distribution influence

Merits

Strength

The method provides a novel framework for analyzing input-dependent model behavior, with potential applications in natural language processing and beyond.

Strength

The use of log-likelihood vectors and model maps offers a new perspective on comparing language models and their performance.

Strength

The introduction of pointwise mutual information vectors enhances the framework's ability to account for unconditional distribution influence.

Demerits

Limitation

The method's reliance on log-likelihood vectors may be computationally intensive and require significant training data.

Expert Commentary

This article makes a significant contribution to the field of natural language processing by providing a novel framework for analyzing input-dependent model behavior. The use of log-likelihood vectors and model maps offers a new perspective on comparing language models and their performance. The introduction of pointwise mutual information vectors enhances the framework's ability to account for unconditional distribution influence. However, the method's reliance on log-likelihood vectors may be computationally intensive and require significant training data. Nevertheless, the implications of this work are far-reaching, with potential applications in model interpretability, natural language processing, and the responsible development and deployment of language models.

Recommendations

  • ✓ Further research is needed to evaluate the scalability and computational feasibility of the method.
  • ✓ The framework's potential applications in model interpretability and natural language processing should be explored in more detail.

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