LLM2Loss: Leveraging Language Models for Explainable Model Diagnostics

• URL: http://arxiv.org/abs/2305.03212v2
• Date: Wed, 17 May 2023 22:36:03 GMT
• Title: LLM2Loss: Leveraging Language Models for Explainable Model Diagnostics
• Authors: Shervin Ardeshir
• Abstract summary: We propose an approach that can provide semantic insights into a model's patterns of failures and biases. We show that an ensemble of such lightweight models can be used to generate insights on the performance of the black-box model.
• Score: 5.33024001730262
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trained on a vast amount of data, Large Language models (LLMs) have achieved unprecedented success and generalization in modeling fairly complex textual inputs in the abstract space, making them powerful tools for zero-shot learning. Such capability is extended to other modalities such as the visual domain using cross-modal foundation models such as CLIP, and as a result, semantically meaningful representation are extractable from visual inputs. In this work, we leverage this capability and propose an approach that can provide semantic insights into a model's patterns of failures and biases. Given a black box model, its training data, and task definition, we first calculate its task-related loss for each data point. We then extract a semantically meaningful representation for each training data point (such as CLIP embeddings from its visual encoder) and train a lightweight diagnosis model which maps this semantically meaningful representation of a data point to its task loss. We show that an ensemble of such lightweight models can be used to generate insights on the performance of the black-box model, in terms of identifying its patterns of failures and biases.

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