This paper is a research review on the interpretability of large language models (LLMs) written by Haiyan Zhao et al., titled "Explainability for Large Language Models: A Survey". The following is a detailed summary of the paper:
Summary
Large Language Models (LLMs) perform well in natural language processing (NLP) tasks, but their internal mechanisms are opaque, which brings risks to downstream applications.
The paper proposes a taxonomy of interpretability techniques and provides a structured overview of approaches for Transformer-based language models.
The paper classifies techniques based on the training paradigm of LLMs (traditional fine-tuning paradigm and suggestion paradigm) and discusses metrics for evaluating generated explanations and how to leverage explanations to debug models and improve performance.
Finally, the paper explores the main challenges and emerging opportunities facing explanation techniques in the era of LLMs compared to traditional deep learning models.
1 Introduction
LLMs such as BERT, GPT-3, GPT-4, etc. have been used in commercial products, but their complex "black box" system characteristics make model interpretation more challenging.
Explainability is critical for building user trust and helping researchers identify biases, risks, and areas for performance improvement.
2. Training paradigm for LLMs
The two main training paradigms for LLMs are introduced: the traditional fine-tuning paradigm and the cueing paradigm, and it is pointed out that different paradigms require different types of explanations.
3. Explanation of the Traditional Fine-tuning Paradigm
Methods for providing LLMs with both local explanations (for individual predictions) and global explanations (for overall knowledge of the model) are discussed.
Local explanations include feature attribution, attention mechanism, example basis, and natural language explanation.
Global explanations focus on understanding the inner workings of the model and include probe methods, neuronal activation analysis, conceptual foundations approaches, and mechanistic explanations.
4. Explanation of the Prompt Model
For hint-based models, new explanation techniques are discussed, such as chain-of-thought (CoT) explanation and leveraging the reasoning and explanation capabilities of LLMs themselves to improve the prediction performance.
5. Explain the evaluation
Two main dimensions for evaluating explanations are discussed: plausibility to humans and fidelity in capturing the internal logic of LLMs.
Different metrics and methods for evaluating local explanations and CoT explanations are introduced.
6. Research Challenges
We explore key issues that require further investigation in interpretability research, including the lack of benchmark datasets with real explanations, emerging sources of power of LLMs, comparisons of different paradigms, shortcut learning of LLMs, attention redundancy, the transition from snapshot explanations to temporal analysis, and safety and ethical issues.
7. Conclusion
The paper summarizes the main development directions of LLMs interpretability techniques and emphasizes that as LLMs develop, interpretability is crucial to ensure the transparency, fairness, and beneficial nature of these models.
references
A series of citations to relevant research are provided, covering areas such as interpretability, machine learning algorithms, and natural language processing.
Overall, this paper provides a comprehensive framework for understanding and interpreting large language models and highlights the importance of considering interpretability when developing and deploying these powerful tools.
