Title: Explainable AI in NLP: Towards a Comprehensive Framework for Interpretable Neural Language Models
Natural Language Processing (NLP) is a rapidly growing field that has seen many recent advancements due to the emergence of deep learning-based models. These models, such as transformer-based neural language models, have achieved state-of-the-art results in various NLP tasks, including language modeling, machine translation, and sentiment analysis. However, these models are often considered “black boxes” because their inner workings are not transparent, making it difficult to interpret how they arrive at their decisions. This lack of transparency hinders the ability to trust and understand these models, which can be critical in applications where errors can have significant consequences. Explainable AI (XAI) aims to address this issue by developing interpretable models that can provide insights into the decision-making process of these models.
The problem of interpreting neural language models is becoming increasingly important in the NLP community. Although some approaches have been proposed to provide explanations for these models, they are often limited in scope and fail to provide a comprehensive understanding of how these models make decisions. Furthermore, the current research lacks a clear framework for interpreting and evaluating these models, making it difficult to compare different approaches and assess their effectiveness.
This research proposal aims to develop a comprehensive framework for interpreting neural language models. The proposed framework will address the following objectives:
- Develop an overview of the current approaches for interpreting neural language models, including their strengths and weaknesses.
- Identify the key factors that contribute to the interpretability of these models, such as model architecture, data representation, and evaluation metrics.
- Develop new methods for interpreting neural language models that leverage recent advancements in explainable AI, such as attention-based mechanisms and counterfactual analysis.
- Evaluate the effectiveness of the proposed framework in improving the interpretability of neural language models across different NLP tasks, including language modeling, sentiment analysis, and machine translation.
To achieve the above objectives, this research proposal will use a combination of quantitative and qualitative research methods, including:
- Literature review: A systematic review of the current approaches for interpreting neural language models will be conducted to identify the strengths and weaknesses of existing methods.
- Framework development: Based on the literature review, a comprehensive framework for interpreting neural language models will be developed.
- Method development: New methods for interpreting neural language models will be proposed based on recent advancements in explainable AI.
- Model evaluation: The proposed framework and methods will be evaluated on several NLP tasks, including language modeling, sentiment analysis, and machine translation, using standard evaluation metrics such as accuracy, F1 score, and interpretability metrics such as fidelity and perturbation analysis.
This research proposal expects to develop a comprehensive framework for interpreting neural language models that can be used across different NLP tasks. The proposed framework will provide insights into the decision-making process of these models and improve their transparency, which can be critical in applications where errors can have significant consequences.
Furthermore, this research proposal aims to contribute to the development of explainable AI, which has the potential to transform how AI systems are developed and used in various domains.