@inproceedings{
singh2023explaining,
title={Explaining black box text modules in natural language with language models},
author={Chandan Singh and Aliyah Hsu and Richard Antonello and Shailee Jain and Alexander Huth and Bin Yu and Jianfeng Gao},
booktitle={XAI in Action: Past, Present, and Future Applications},
year={2023},
url={https://openreview.net/forum?id=3BX9tM03GT}
}
Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have created a growing need for interpretability. Here, we ask whether we can automatically obtain natural language explanations for black box text modules. A *text module* is any function that maps text to a scalar continuous value, such as a submodule within an LLM or a fitted model of a brain region. *Black box* indicates that we only have access to the module's inputs. We introduce Summarize and Score (SASC), a method that takes in a text module and returns a natural language explanation of the module's selectivity along with a score for how reliable the explanation. We study SASC in 2 contexts. First, we evaluate SASC on synthetic modules and find that it often recovers ground truth explanations. Second, we use SASC to explain modules found within a pre-trained BERT model, enabling inspection of the model's internals.
Explaining black box text modules in natural language with language models
Chandan Singh Aliyah Hsu Richard Antonello Shailee Jain Alexander Huth Bin Yu Jianfeng Gao