Black-box language model explanation by context length probing

Anonymous

Keywords: Transformer, language models, interpretability, explainability, long-range dependencies

Abstract: The increasingly widespread adoption of large Transformer 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.

Paper Type: short

Research Area: Interpretability and Analysis of Models for NLP