activity
20232026
most citedError Discovery by Clustering Influence Embeddings

3 citations · 5 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CR2026

MAStrike: Shapley-Guided Collusive Red-Teaming on Multi-Agent Systems

Chejian Xu, Zhaorun Chen, Jingyang Zhang +5

Hierarchical multi-agent systems (MAS) are rapidly being deployed in high-stakes workflows across domains such as finance and software engineering. In these systems, safety and sec…

cs.CY2025

Enduring Disparities in the Workplace: A Pilot Study in the AI Community

Yunusa Simpa Abdulsalam, Siobhan Mackenzie Hall, Ana Quintero-Ossa +6

In efforts toward achieving responsible artificial intelligence (AI), fostering a culture of workplace transparency, diversity, and inclusion can breed innovation, trust, and emplo…

cs.CL2024

XForecast: Evaluating Natural Language Explanations for Time Series Forecasting

Taha Aksu, Chenghao Liu, Amrita Saha +3

Time series forecasting aids decision-making, especially for stakeholders who rely on accurate predictions, making it very important to understand and explain these models to ensur…

cs.CL2024

Evaluating Cultural and Social Awareness of LLM Web Agents

Haoyi Qiu, Alexander R. Fabbri, Divyansh Agarwal +4

As large language models (LLMs) expand into performing as agents for real-world applications beyond traditional NLP tasks, evaluating their robustness becomes increasingly importan…

cs.CL2024★ 2 cited

MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases

Rithesh Murthy, Liangwei Yang, Juntao Tan +15

The deployment of Large Language Models (LLMs) and Large Multimodal Models (LMMs) on mobile devices has gained significant attention due to the benefits of enhanced privacy, stabil…

cs.LG2023★ 3 cited

Error Discovery by Clustering Influence Embeddings

Fulton Wang, Julius Adebayo, Sarah Tan +2

We present a method for identifying groups of test examples -- slices -- on which a model under-performs, a task now known as slice discovery. We formalize coherence -- a requireme…