44 citations · 92 across the 11 of their papers we have counts for
11 papers
Contextualized Privacy Defense for LLM Agents
Yule Wen, Yanzhe Zhang, Jianxun Lian +3
LLM agents increasingly act on users' personal information, yet existing privacy defenses remain limited in both design and adaptability. Most prior approaches rely on static or pa…
Towards Execution-Grounded Automated AI Research
Chenglei Si, Zitong Yang, Yejin Choi +3
Automated AI research holds great potential to accelerate scientific discovery. However, current LLMs often generate plausible-looking but ineffective ideas. Execution grounding ma…
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…
Searching for Privacy Risks in LLM Agents via Simulation
Yanzhe Zhang, Diyi Yang
The widespread deployment of LLM-based agents is likely to introduce a critical privacy threat: malicious agents that proactively engage others in multi-turn interactions to extrac…
The Ideation-Execution Gap: Execution Outcomes of LLM-Generated versus Human Research Ideas
Chenglei Si, Tatsunori Hashimoto, Diyi Yang
Large Language Models (LLMs) have shown promise in accelerating the scientific research pipeline. A key capability for this process is the ability to generate novel research ideas,…
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…
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
Chenglei Si, Diyi Yang, Tatsunori Hashimoto
Recent advancements in large language models (LLMs) have sparked optimism about their potential to accelerate scientific discovery, with a growing number of works proposing researc…
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…