Learning Two-Branch Neural Networks for Image-Text Matching Tasks. - Liwei Wang 009; Yin Li 003; Jing Huang 014; Svetlana Lazebnik (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Improving Weakly Supervised Visual Grounding by Contrastive Knowledge Distillation. - Liwei Wang 009; Jing Huang 014; Yin Li 003; Kun Xu 005; Zhengyuan Yang; Dong Yu 001 (CVPR, 2021)

Inducing Character-level Structure in Subword-based Language Models with Type-level Interchange Intervention Training. - Jing Huang 014; Zhengxuan Wu; Kyle Mahowald; Christopher Potts (ACL (Findings), 2023)

Rigorously Assessing Natural Language Explanations of Neurons. - Jing Huang 014; Atticus Geiger; Karel D'Oosterlinck; Zhengxuan Wu; Christopher Potts (BlackboxNLP@EMNLP, 2023)

Culturally Aware Natural Language Inference. - Jing Huang 014; Diyi Yang (EMNLP (Findings), 2023)

RAVEL: Evaluating Interpretability Methods on Disentangling Language Model Representations. - Jing Huang 014; Zhengxuan Wu; Christopher Potts; Mor Geva; Atticus Geiger (ACL (1), 2024)

Demystifying Verbatim Memorization in Large Language Models. - Jing Huang 014; Diyi Yang; Christopher Potts (EMNLP, 2024)

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions. - Zhengxuan Wu; Atticus Geiger; Aryaman Arora; Jing Huang 014; Zheng Wang 078; Noah D. Goodman; Christopher D. Manning; Christopher Potts (NAACL (Demonstrations), 2024)

A Reply to Makelov et al. (2023)'s "Interpretability Illusion" Arguments. - Zhengxuan Wu; Atticus Geiger; Jing Huang 014; Aryaman Arora; Thomas Icard; Christopher Potts; Noah D. Goodman (CoRR, 2024)

HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks. - Jiuding Sun; Jing Huang 014; Sidharth Baskaran; Karel D'Oosterlinck; Christopher Potts; Michael Sklar; Atticus Geiger (ICLR, 2025)

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders. - Zhengxuan Wu; Aryaman Arora; Atticus Geiger; Zheng Wang 078; Jing Huang 014; Dan Jurafsky; Christopher D. Manning; Christopher Potts (CoRR, 2025)

Internal Causal Mechanisms Robustly Predict Language Model Out-of-Distribution Behaviors. - Jing Huang 014; Junyi Tao; Thomas Icard; Diyi Yang; Christopher Potts (CoRR, 2025)