Fine-grained Interpretation and Causation Analysis in Deep NLP Models
This paper is a write-up for the tutorial on "Fine-grained Interpretation and Causation Analysis in Deep NLP Models" that we are presenting at NAACL 2021. We present and discuss the research work on interpreting fine-grained components of a model from two perspectives, i) fine-grained interpretation, ii) causation analysis. The former introduces methods to analyze individual neurons and a group of neurons with respect to a language property or a task. The latter studies the role of neurons and input features in explaining decisions made by the model. We also discuss application of neuron analysis such as network manipulation and domain adaptation. Moreover, we present two toolkits namely NeuroX and Captum, that support functionalities discussed in this tutorial.
Code (0)
등록된 구현이 없습니다.
Tasks
Domain AdaptationSimilar Papers 제목 키워드 기반
From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations
This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations to identify socioeconomic factors influencin…
Causal InferenceDecision MakingReview on Causality Detection Based on Empirical Dynamic Modeling
In contemporary scientific research, understanding the distinction between correlation and causation is crucial. While correlation is a widely used analytical standard, it does not inherently imply causation. This paper …
Time SeriesCorrelation vs causation in Alzheimer's disease: an interpretability-driven study
Understanding the distinction between causation and correlation is critical in Alzheimer's disease (AD) research, as it impacts diagnosis, treatment, and the identification of true disease drivers. This experiment invest…
Causal InferenceFeature ImportanceA Critique on the Interventional Detection of Causal Relationships
Interventions are of fundamental importance in Pearl's probabilistic causality regime. In this paper, we will inspect how interventions influence the interpretation of causation in causal models in specific situation. To…
validA Study of the Bump Alternation in Japanese from the Perspective of Extended/Onset Causation
This paper deals with a seldom studied object/oblique alternation phenomenon in Japanese, which. We call this the bump alternation. This phenomenon, first discussed by Sadanobu (1990), is similar to the English with/agai…
Object