6 citations · 36 across the 23 of their papers we have counts for
24 papers
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley Values
Stephanie Schoch, Ritwick Mishra, Yangfeng Ji
Although Shapley values have been shown to be highly effective for identifying harmful training instances, dataset size and model complexity constraints limit the ability to apply…
Pre-training Transformers for Knowledge Graph Completion
Sanxing Chen, Hao Cheng, Xiaodong Liu +3
Learning transferable representation of knowledge graphs (KGs) is challenging due to the heterogeneous, multi-relational nature of graph structures. Inspired by Transformer-based p…
Improving Interpretability via Explicit Word Interaction Graph Layer
Arshdeep Sekhon, Hanjie Chen, Aman Shrivastava +3
Recent NLP literature has seen growing interest in improving model interpretability. Along this direction, we propose a trainable neural network layer that learns a global interact…
Identifying the Source of Vulnerability in Explanation Discrepancy: A Case Study in Neural Text Classification
Ruixuan Tang, Hanjie Chen, Yangfeng Ji
Some recent works observed the instability of post-hoc explanations when input side perturbations are applied to the model. This raises the interest and concern in the stability of…
CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification
Stephanie Schoch, Haifeng Xu, Yangfeng Ji
Data valuation, or the valuation of individual datum contributions, has seen growing interest in machine learning due to its demonstrable efficacy for tasks such as noisy label det…
Balanced Adversarial Training: Balancing Tradeoffs between Fickleness and Obstinacy in NLP Models
Hannah Chen, Yangfeng Ji, David Evans
Traditional (fickle) adversarial examples involve finding a small perturbation that does not change an input's true label but confuses the classifier into outputting a different pr…