Questioning the Explainability of Large Language Models

Core Concepts

Large language models' explainability is sensitive to training randomness, challenging simple explanations.

Abstract

The explainability of large language models has been a topic of concern due to their tradeoff between performance and explainability. This paper questions the feasibility of providing simple and informative explanations for such models by characterizing the sensitivity of explanations to training randomness. The study highlights that word-level univariate explanations from simpler models carry more signal and less noise compared to transformer-based models. By proposing alternative definitions for signal and noise, the paper aims to improve the informativeness of explanations while considering the plausibility for readers. The research emphasizes the need for more quantitative views on explainability in large language models.

Stats

"The accuracy was evaluated on a test set of 1,000 news." "The accuracy of the feature-based model was slightly below that of the transformer-based one (≈ 89% vs. ≈ 96%)." "The SNR values reached below one, indicating larger variance in weights assigned by individual explanations compared to average weights."

Quotes

"An explanation should accurately reflect the reasoning process behind the model's prediction." "Explanations limited to a single model are insufficient if equivalent models have different explanations." "Combining LLMs with better explainability may require contradicting plausibility assumptions."

Key Insights Distilled From

A Question on the Explainability of Large Language Models and the Word-Level Univariate First-Order Plausibility Assumption

by Jeremie Boga... at arxiv.org 03-18-2024

https://arxiv.org/pdf/2403.10275.pdf

Deeper Inquiries

How can complex explanations improve LLMs' explainability without compromising simplicity

Complex explanations can enhance the explainability of Large Language Models (LLMs) by capturing more nuanced relationships and patterns in the data. While simple explanations like word-level, univariate first-order analyses may provide a basic understanding, they often lack depth and fail to capture complex interactions within the model. By incorporating t-tuples of words, d attention values per word, or higher-order statistics into explanations, LLMs can offer more detailed insights into how decisions are made. These complex explanations can reveal intricate dependencies between words or features that contribute to predictions, leading to a richer understanding of the model's inner workings. Despite their complexity, these advanced explanation methods have the potential to improve interpretability without sacrificing simplicity entirely. By carefully designing visualization techniques or interactive tools that present these complex explanations in an intuitive manner, users can still grasp key insights without being overwhelmed by technical details. Additionally, providing clear narratives or summaries alongside detailed visualizations can help bridge the gap between complexity and simplicity in explaining LLMs' decisions.

Are there other criteria besides faithfulness and plausibility that should be considered in evaluating model explanations

In evaluating model explanations beyond faithfulness and plausibility, several other criteria should be considered to ensure comprehensive assessment: Consistency: Explanations should be consistent across similar inputs or models under varying conditions. Robustness: Explanations should remain stable when minor changes are made to input data or model parameters. Relevance: The provided explanations should focus on relevant aspects of the input data that influence model predictions. Completeness: Explanations should cover all significant factors contributing to a prediction rather than oversimplifying or omitting crucial information. Transparency: The process used for generating explanations should be transparent and understandable for non-experts. By considering these additional criteria alongside faithfulness and plausibility measures, a more holistic evaluation of model explainability can be achieved.

How can semantic definitions of signal and noise impact our understanding of LLMs' explainability

Semantic definitions of signal and noise play a crucial role in shaping our understanding of LLMs' explainability by offering insights beyond statistical metrics alone: Signal Interpretation: Semantic definitions allow us to interpret signal not just as variance but as meaningful information captured by the explanation method—highlighting important features influencing predictions. Noise Analysis: Semantic perspectives on noise help identify irrelevant fluctuations due to training randomness versus genuine uncertainties inherent in decision-making processes within LLMs. Contextual Relevance: Semantic considerations enable us to assess whether variations observed in explanations align with contextual relevance—ensuring that noisy elements do not detract from interpretable signals. By integrating semantic definitions into our analysis framework for LLM explainability assessments, we gain deeper insights into how well models capture essential features while filtering out extraneous influences introduced during training processes such as random initialization seeds or optimization algorithms' stochastic nature."