Applied Explainability for Large Language Models: A Comparative Study
Summary: arXiv:2604.15371v1 Announce Type: cross
Large language models (LLMs) have demonstrated remarkable capabilities in various natural language processing (NLP) tasks. However, the intricacies of their decision-making processes often remain opaque. This lack of transparency poses significant challenges in terms of trust, debugging, and the practical deployment of these models in real-world applications.
This article discusses a comparative study that evaluates three prominent explainability techniques applied to a fine-tuned DistilBERT model focused on sentiment classification tasks, specifically the SST-2 dataset. The techniques under scrutiny include:
- Integrated Gradients
- Attention Rollout
- SHAP (SHapley Additive exPlanations)
The primary objective of this study is not to introduce novel methodologies but rather to assess the effectiveness and practical implications of existing explainability approaches within a consistent and reproducible framework.
Key Findings
The results from the comparative study yield several noteworthy insights regarding the performance and usability of the examined methods:
- Gradient-based Attribution: Techniques such as Integrated Gradients provide stable and intuitive explanations. They tend to align closely with human understanding, making them particularly valuable for debugging and interpretability.
- Attention-based Methods: While methods like Attention Rollout are computationally efficient, they often fail to correlate with the features most relevant to the model’s predictions. This misalignment can lead to misleading interpretations of the model’s behavior.
- Model-agnostic Approaches: Techniques such as SHAP offer flexibility in application across various model architectures. However, they also introduce higher computational costs and variability in their outputs, which may complicate their usability in certain contexts.
Trade-offs in Explainability
This research underscores the critical trade-offs that exist between different explainability methods. It emphasizes that while these techniques can serve as useful diagnostic tools, they should not be viewed as definitive explanations of model behavior. The findings advocate for a nuanced understanding of explainability in the context of transformer-based NLP systems.
Researchers and engineers working with LLMs can leverage the insights from this study to make more informed decisions regarding the selection and application of explainability methods in their work. As the field of NLP continues to evolve, the significance of explainability will only increase, necessitating ongoing evaluation of existing techniques and the development of new ones.
This article is a preprint and has not yet undergone peer review, suggesting that while the findings are promising, further validation in peer-reviewed contexts will be essential for solidifying these conclusions.