Melkamu Mersha, Mingiziem Bitewa, Tsion Abay, Jugal Kalita
Abstract: Neural networks are widely regarded as black-box models, creating significant challenges in understanding their inner workings, especially in natural language processing (NLP) applications. To address this opacity, model explanation techniques like Local Interpretable Model-Agnostic Explanations (LIME) have emerged as essential tools for providing insights into the behavior of these complex systems. This study leverages LIME to interpret a multi-layer perceptron (MLP) neural network trained on a text classification task. By analyzing the contribution of individual features to model predictions, the LIME approach enhances interpretability and supports informed decision-making. Despite its effectiveness in offering localized explanations, LIME has limitations in capturing global patterns and feature interactions. This research highlights the strengths and shortcomings of LIME and proposes directions for future work to achieve more comprehensive interpretability in neural NLP models.
Keywords: Explainable AI, Neural Networks, Natural Language Processing, interpretability, black box models, explainability techniques, LIME, explainable machine learning
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2412.18036 [cs.CL] |
| (or arXiv:2412.18036v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2412.18036Focus to learn more |
Submission history
From: Melkamu Mersha [view email]
[v1] Mon, 23 Dec 2024 23:09:56 UTC (376 KB)
[v2] Wed, 8 Jan 2025 19:44:56 UTC (7,235 KB)
The article describes a research study focused on improving the interpretability of neural networks used in natural language processing (NLP), specifically in text classification tasks. Since neural networks—often considered black boxes—are difficult to understand, the study employs Local Interpretable Model-Agnostic Explanations (LIME) to shed light on how individual features influence the network’s predictions.
Key points include:
The article clearly states the problem—neural networks as black-box models—and the motivation to improve interpretability, particularly in NLP. The use of LIME as an explanation method is clearly introduced.
Focus and Scope:
It concisely explains the study’s approach: applying LIME to interpret a multi-layer perceptron (MLP) for text classification. This keeps the scope focused and understandable.
Balanced Discussion:
The article not only highlights the benefits of LIME, such as localized feature contribution analysis, but also honestly addresses its limitations regarding global pattern recognition and feature interactions.
Future Directions:
It ends by suggesting there is room for further research, indicating awareness that interpretability is an ongoing challenge.
Areas for Improvement:
Specificity and Impact:
The abstract could be improved by including specific findings or insights gained from applying LIME. For example, what particular aspects of the MLP’s decision-making were uncovered? How did it support “informed decision-making” concretely?
Novelty and Contribution:
While using LIME for interpretability is useful, it’s a known technique. The abstract could better emphasize what is novel in the study—whether it’s the dataset used, insights about MLPs, or a comparison with other interpretability techniques.
Technical Detail:
A brief mention of the dataset or text classification task (e.g., sentiment analysis, topic classification) could provide more context to readers.
Language Precision:
Phrases like “widely regarded as black-box models” and “essential tools” are somewhat generic. More precise language, quantification, or references could improve the scientific tone.
Overall:
The article is well-written and gives a coherent overview of the study’s aims and outcomes. It effectively communicates the motivation to use LIME for interpreting a neural NLP model and acknowledges its limitations. However, it could be strengthened by including more specific results, clearer articulation of novelty, and some additional technical context to better engage expert readers.
If this is for a conference or journal submission, consider enhancing the abstract to explicitly state the key contributions and findings for greater impact.Problem: Neural networks in NLP are complex and hard to interpret.
Approach: Use LIME to generate local explanations for a multi-layer perceptron (MLP) model.
Findings: LIME effectively reveals feature contributions for specific predictions, aiding interpretability and decision-making.
Limitations: LIME struggles with capturing global model behavior and interactions between features.
Future Directions: The study suggests enhancing interpretability methods to better understand overall patterns and feature interplay in NLP models.
In summary, the research demonstrates how LIME can help demystify neural NLP models at a local level while acknowledging the need for complementary techniques to obtain a global understanding.
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