Model explanations that shed light on the
model’s predictions are becoming a desired additional output of NLP models, alongside their
predictions. Challenges in creating these explanations include making them trustworthy
and faithful to the model’s predictions. In this
work, we propose a novel framework for guiding model explanations by supervising them explicitly. To this end, our method, LEXPLAIN,
uses task-related lexicons to directly supervise
model explanations. This approach consistently
improves the plausibility of model’s explanations without sacrificing performance on the
task, as we demonstrate on sentiment analysis
and toxicity detection. Our analyses show that
our method also demotes spurious correlations
(i.e., with respect to African American English
dialect) on toxicity detection, improving fairness.
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Explanation-based Finetuning Makes Models More Robust to Spurious Cues
Large Language Models (LLMs) are so powerful that they sometimes learn correlations between labels and features that are irrelevant to the task, leading to poor generalization on out-of-distribution data. We propose explanation-based finetuning as a general approach to mitigate LLMs’ reliance on spurious correlations. Unlike standard finetuning where the model only predicts the answer given the input, we finetune the model to additionally generate a free-text explanation supporting its answer. To evaluate our method, we finetune the model on artificially constructed training sets containing different types of spurious cues, and test it on a test set without these cues. Compared to standard finetuning, our method makes GPT-3 (davinci) remarkably more robust against spurious cues in terms of accuracy drop across four classification tasks: ComVE (+1.2), CREAK (+9.1), e-SNLI (+15.4), and SBIC (+6.5). The efficacy generalizes across multiple model families and scales, with greater gains for larger models. Finally, our method also works well with explanations generated by the model, implying its applicability to more datasets without human-written explanations.
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- Award ID(s):
- 1928474
- PAR ID:
- 10463288
- Date Published:
- Journal Name:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics
- Volume:
- 1
- Page Range / eLocation ID:
- 4420 to 4441
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2023.acl-long.242
