Name
Title | ||
|---|---|---|
SparCAssist: A Model Risk Assessment Assistant Based on Sparse Generated Counterfactuals |
Abstract | ||
|---|---|---|
We introduce SparCAssist, a general-purpose risk assessment tool for the machine learning models trained for language tasks. It evaluates models' risk by inspecting their behavior on counterfactuals, namely out-of-distribution instances generated based on the given data instance. The counterfactuals are generated by replacing tokens in rational subsequences identified by ExPred, while the replacements are retrieved using HotFlip or the Masked-Language-Model-based algorithms. The main purpose of our system is to help the human annotators to assess the model's risk on deployment. The counterfactual instances generated during the assessment are the by-product and can be used to train more robust NLP models in the future. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1145/3477495.3531677 | SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval |
Keywords | DocType | Citations |
Interpretable Machine Learning, Counterfactual Interpretation, Dataannotation tools, Human-in-the-loop Machine Learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zijian Zhang | 1 | 0 | 0.34 |
| Vinay Setty | 2 | 0 | 0.34 |
| Avishek Anand | 3 | 3 | 1.18 |