no code implementations • NAACL (TrustNLP) 2022 • Brihi Joshi, Aaron Chan, Ziyi Liu, Xiang Ren
For the latter, explanation regularization (ER) aims to improve NLM generalization by pushing the machine rationales to align with human rationales.
1 code implementation • 6 Nov 2023 • Sahana Ramnath, Brihi Joshi, Skyler Hallinan, Ximing Lu, Liunian Harold Li, Aaron Chan, Jack Hessel, Yejin Choi, Xiang Ren
Results on five difficult question-answering datasets StrategyQA, QuaRel, OpenBookQA, NumerSense and QASC show that not only does MaRio improve task accuracy, but it also improves the self-rationalization quality of small LMs across the aforementioned axes better than a supervised fine-tuning (SFT) baseline.
1 code implementation • 11 May 2023 • Brihi Joshi, Ziyi Liu, Sahana Ramnath, Aaron Chan, Zhewei Tong, Shaoliang Nie, Qifan Wang, Yejin Choi, Xiang Ren
Existing metrics like task performance of the LM generating the rationales, or similarity between generated and gold rationales are not good indicators of their human utility.
no code implementations • 19 Dec 2022 • Aaron Chan, Zhiyuan Zeng, Wyatt Lake, Brihi Joshi, Hanjie Chen, Xiang Ren
First, KNIFE finetunes a teacher LM (given task input and FTR) to predict the task output, transferring reasoning knowledge from the FTRs to the teacher's hidden states.
no code implementations • 30 Oct 2022 • Dong-Ho Lee, Akshen Kadakia, Brihi Joshi, Aaron Chan, Ziyi Liu, Kiran Narahari, Takashi Shibuya, Ryosuke Mitani, Toshiyuki Sekiya, Jay Pujara, Xiang Ren
Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model.
1 code implementation • 25 May 2022 • Brihi Joshi, Aaron Chan, Ziyi Liu, Shaoliang Nie, Maziar Sanjabi, Hamed Firooz, Xiang Ren
to align with human rationales (Which input tokens would humans focus on?).
1 code implementation • COLING 2020 • Brihi Joshi, Neil Shah, Francesco Barbieri, Leonardo Neves
Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years.
1 code implementation • 23 Jun 2018 • Hridoy Sankar Dutta, Aditya Chetan, Brihi Joshi, Tanmoy Chakraborty
Thus they become customers of blackmarket syndicates and engage in fake activities.
Social and Information Networks
1 code implementation • 11 Apr 2018 • Nishtha Madaan, Gautam Singh, Sameep Mehta, Aditya Chetan, Brihi Joshi
Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically.