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arXiv · 2212.14815

Black-box language model explanation by context length probing

Ondřej Cífka·Antoine Liutkus
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Abstract

The increasingly widespread adoption of large language models has highlighted the need for improving their explainability. We present context length probing, a novel explanation technique for causal language models, based on tracking the predictions of a model as a function of the length of available context, and allowing to assign differential importance scores to different contexts. The technique is model-agnostic and does not rely on access to model internals beyond computing token-level probabilities. We apply context length probing to large pre-trained language models and offer some initial analyses and insights, including the potential for studying long-range dependencies. The source code and an interactive demo of the method are available.

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Ondřej Cífka, Antoine Liutkus. 2022-12-30. Black-box language model explanation by context length probing. https://doi.org/10.18653/v1/2023.acl-short.92

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