19 citations · 34 across the 4 of their papers we have counts for
7 papers
ORCA: Interpreting Prompted Language Models via Locating Supporting Data Evidence in the Ocean of Pretraining Data
Xiaochuang Han, Yulia Tsvetkov
Large pretrained language models have been performing increasingly well in a variety of downstream tasks via prompting. However, it remains unclear from where the model learns the…
Influence Tuning: Demoting Spurious Correlations via Instance Attribution and Instance-Driven Updates
Xiaochuang Han, Yulia Tsvetkov
Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approac…
Fortifying Toxic Speech Detectors Against Veiled Toxicity
Xiaochuang Han, Yulia Tsvetkov
Modern toxic speech detectors are incompetent in recognizing disguised offensive language, such as adversarial attacks that deliberately avoid known toxic lexicons, or manifestatio…
Explaining Black Box Predictions and Unveiling Data Artifacts through Influence Functions
Xiaochuang Han, Byron C. Wallace, Yulia Tsvetkov
Modern deep learning models for NLP are notoriously opaque. This has motivated the development of methods for interpreting such models, e.g., via gradient-based saliency maps or th…
No Permanent Friends or Enemies: Tracking Relationships between Nations from News
Xiaochuang Han, Eunsol Choi, Chenhao Tan
Understanding the dynamics of international politics is important yet challenging for civilians. In this work, we explore unsupervised neural models to infer relations between nati…
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
Xiaochuang Han, Jacob Eisenstein
Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large co…