Affiliation:
1. Computer Science Department, Bar Ilan University
2. Allen Institute for Artificial Intelligence. yanaiela@gmail.com
3. Allen Institute for Artificial Intelligence. shauli.ravfogel@gmail.com
4. Computer Science Department, Bar Ilan University. alonjacovi@gmail.com
5. Allen Institute for Artificial Intelligence. yoav.goldberg@gmail.com
Abstract
Abstract
A growing body of work makes use of probing in order to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability to infer behavioral conclusions from probing results, and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded. Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention that removes it from the representation. Equipped with this new analysis tool, we can ask questions that were not possible before, for example, is part-of-speech information important for word prediction? We perform a series of analyses on BERT to answer these types of questions. Our findings demonstrate that conventional probing performance is not correlated to task importance, and we call for increased scrutiny of claims that draw behavioral or causal conclusions from probing results.1
Reference43 articles.
1. Fine-grained analysis of sentence embeddings using auxiliary prediction tasks;Adi;CoRR,2016
2. Probing linguistic features of sentence-level representations in relation extraction;Alt,2020
3. Experiment tracking with weights and biases;Biewald,2020
4. What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties;Conneau,2018
5. BERT: Pre-training of deep bidirectional transformers for language understanding;Devlin,2019
Cited by
35 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献