27 citations · 87 across the 22 of their papers we have counts for
14 papers · 1 filter
The Illusion of Robustness: Aggregate Accuracy Hides Prediction Flips under Task-Irrelevant Context
Yanzhe Zhang, Sanmi Koyejo, Diyi Yang
As large language models (LLMs) grow more capable, they are increasingly deployed in context-rich settings where task inputs are often accompanied by long, partially irrelevant con…
AutoMetrics: Approximate Human Judgements with Automatically Generated Evaluators
Michael J. Ryan, Yanzhe Zhang, Amol Salunkhe +3
Evaluating user-facing AI applications remains a central challenge, especially in open-ended domains such as travel planning, clinical note generation, or dialogue. The gold standa…
Generative Interfaces for Language Models
Jiaqi Chen, Yanzhe Zhang, Yutong Zhang +2
Large language models (LLMs) are increasingly seen as assistants, copilots, and consultants, capable of supporting a wide range of tasks through natural conversation. However, most…
Attacking Vision-Language Computer Agents via Pop-ups
Yanzhe Zhang, Tao Yu, Diyi Yang
Autonomous agents powered by large vision and language models (VLM) have demonstrated significant potential in completing daily computer tasks, such as browsing the web to book tra…
Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping
Ryan Li, Yanzhe Zhang, Diyi Yang
Sketches are a natural and accessible medium for UI designers to conceptualize early-stage ideas. However, existing research on UI/UX automation often requires high-fidelity inputs…
Distilling an End-to-End Voice Assistant Without Instruction Training Data
William Held, Ella Li, Michael Ryan +3
Voice assistants, such as Siri and Google Assistant, typically model audio and text separately, resulting in lost speech information and increased complexity. Recent efforts to add…
Best Practices and Lessons Learned on Synthetic Data
Ruibo Liu, Jerry Wei, Fangyu Liu +8
The success of AI models relies on the availability of large, diverse, and high-quality datasets, which can be challenging to obtain due to data scarcity, privacy concerns, and hig…
Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering
Chenglei Si, Yanzhe Zhang, Ryan Li +3
Generative AI has made rapid advancements in recent years, achieving unprecedented capabilities in multimodal understanding and code generation. This can enable a new paradigm of f…
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
Zijun Liu, Yanzhe Zhang, Peng Li +2
Recent studies show that collaborating multiple large language model (LLM) powered agents is a promising way for task solving. However, current approaches are constrained by using…
Bounding the Capabilities of Large Language Models in Open Text Generation with Prompt Constraints
Albert Lu, Hongxin Zhang, Yanzhe Zhang +2
The limits of open-ended generative models are unclear, yet increasingly important. What causes them to succeed and what causes them to fail? In this paper, we take a prompt-centri…
Robustness of Demonstration-based Learning Under Limited Data Scenario
Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang +1
Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonst…
Continual Sequence Generation with Adaptive Compositional Modules
Yanzhe Zhang, Xuezhi Wang, Diyi Yang
Continual learning is essential for real-world deployment when there is a need to quickly adapt the model to new tasks without forgetting knowledge of old tasks. Existing work on c…
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition
Aaron Reich, Jiaao Chen, Aastha Agrawal +2
Named Entity Recognition (NER) systems often demonstrate great performance on in-distribution data, but perform poorly on examples drawn from a shifted distribution. One way to eva…
Continual Learning for Text Classification with Information Disentanglement Based Regularization
Yufan Huang, Yanzhe Zhang, Jiaao Chen +2
Continual learning has become increasingly important as it enables NLP models to constantly learn and gain knowledge over time. Previous continual learning methods are mainly desig…