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05/26/2023
Theoretical and Practical Perspectives on what Influence Functions Do
Influence functions (IF) have been seen as a technique for explaining mo...
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04/28/2023
Dissecting Recall of Factual Associations in Auto-Regressive Language Models
Transformer-based language models (LMs) are known to capture factual kno...
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02/27/2023
Make Every Example Count: On Stability and Utility of Self-Influence for Learning from Noisy NLP Datasets
Increasingly larger datasets have become a standard ingredient to advanc...
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11/10/2022
Understanding Text Classification Data and Models Using Aggregated Input Salience
Realizing when a model is right for a wrong reason is not trivial and re...
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01/27/2022
Diagnosing AI Explanation Methods with Folk Concepts of Behavior
When explaining AI behavior to humans, how is the communicated informati...
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11/14/2021
"Will You Find These Shortcuts?" A Protocol for Evaluating the Faithfulness of Input Salience Methods for Text Classification
Feature attribution a.k.a. input salience methods which assign an import...
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10/12/2020
Controlled Hallucinations: Learning to Generate Faithfully from Noisy Data
Neural text generation (data- or text-to-text) demonstrates remarkable p...
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10/12/2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods?
There is a recent surge of interest in using attention as explanation of...
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05/01/2020
We Need to Talk About Random Splits
Gorman and Bedrick (2019) recently argued for using random splits rather...
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09/24/2018
Sentence-Level Fluency Evaluation: References Help, But Can Be Spared!
Motivated by recent findings on the probabilistic modeling of acceptabil...
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04/21/2018
Eval all, trust a few, do wrong to none: Comparing sentence generation models
In this paper, we study recent neural generative models for text generat...
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10/28/2015
