Automated Discovery of Interpretable Concepts for Text Classification with Iterative Refinement

Core Concepts

Text Bottleneck Models (TBM) provide both global and local interpretability for text classification by automatically discovering a sparse set of salient concepts and using them to make predictions.

Abstract

The paper introduces Text Bottleneck Models (TBM), a framework for interpretable text classification that automatically discovers a set of salient concepts and uses them to make predictions.
The key components of TBM are:

Concept Generation: An iterative process that uses a large language model (LLM) to discover a sparse set of discriminative concepts from the training data. This avoids the need for manual concept curation.
Concept Measurement: The LLM is used to measure the value of each concept for a given input text in a zero-shot manner, providing numerical scores.
Prediction Layer: A white-box linear layer that combines the concept scores to make the final prediction.

The authors evaluate TBM on 12 diverse text understanding datasets, including sentiment analysis, natural language inference, and topic classification. TBM performs competitively with strong black-box baselines like finetuned BERT and GPT-3.5 on sentiment tasks, but lags behind state-of-the-art models.
A detailed human evaluation reveals that the Concept Generation module can consistently generate high-quality concepts, but occasionally struggles with redundancy and leakage. The Concept Measurement module is found to be highly accurate for sentiment analysis, but more challenging for specialized domains like fake news detection.
The interpretable structure of TBM also allows for novel analysis, such as visualizing the learning curves of different concepts and using them to identify potential biases in the data.

Stats

"food for everyone, with 4 generations to feed"
"The ambiance wasn't what I expected"

Quotes

"Black-box deep neural networks excel in text classification, yet their application in high-stakes domains is hindered by their lack of interpretability."
"Concept-based explanations can provide both global and local insights by identifying important concepts across the dataset and localizing how these concepts relate to each individual prediction."

Key Insights Distilled From

Interpretable-by-Design Text Understanding with Iteratively Generated Concept Bottleneck

by Josh Magnus ... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2310.19660.pdf

Deeper Inquiries

How can the Concept Generation module be further improved to reduce redundancy and leakage?

The Concept Generation module can be enhanced by incorporating additional filters and constraints during the concept discovery process. One approach could involve implementing a mechanism to detect and filter out redundant concepts by comparing new concepts with existing ones. This comparison can be based on semantic similarity or overlap in the information captured by the concepts. Additionally, introducing a validation step where concepts are evaluated for their uniqueness before being added to the concept set can help reduce redundancy.
To address leakage, the Concept Generation module can be refined to prioritize the generation of concepts that are not directly indicative of the classification labels. By incorporating constraints that discourage concepts that directly correlate with the task labels, the system can focus on identifying more abstract and informative concepts that contribute to a deeper understanding of the data. Implementing checks to ensure that concepts do not leak information about the labels can help improve the overall interpretability and reliability of the generated concepts.

What are the limitations of the current TBM structure, and how can it be extended to handle more complex relationships between concepts?

One limitation of the current TBM structure is its reliance on a linear prediction layer, which may not capture complex interactions between concepts. To address this limitation and handle more intricate relationships between concepts, the TBM structure can be extended by incorporating non-linear prediction models such as neural networks. By using neural networks with multiple layers, the model can capture higher-order interactions between concepts, enabling it to learn complex patterns and dependencies in the data.
Furthermore, introducing hierarchical concept structures can help TBM handle more complex relationships between concepts. By organizing concepts into hierarchies or clusters based on their semantic relationships or dependencies, the model can capture nuanced interactions and dependencies between different levels of concepts. This hierarchical approach can enhance the model's ability to represent and interpret complex relationships within the data.

Could the interpretability of TBM be leveraged to provide personalized explanations tailored to the user's background and preferences?

The interpretability of TBM can indeed be leveraged to provide personalized explanations tailored to the user's background and preferences. By incorporating user-specific preferences and constraints into the concept generation and measurement processes, TBM can generate explanations that align with the user's domain knowledge and interests. For example, the system can prioritize concepts that are relevant to the user's expertise or focus on specific aspects of the data that are of interest to the user.
Additionally, TBM can incorporate interactive features that allow users to provide feedback on the generated concepts and explanations. By enabling users to interact with the system and adjust the concepts based on their preferences, TBM can adapt and refine the explanations to better suit the user's needs. This interactive approach can enhance the interpretability of the system and provide personalized explanations that cater to the user's background and preferences.