47 citations · 78 across the 19 of their papers we have counts for
9 papers · 1 filter
Memorilla: Latent Semantic Memory for LLMs
Adibvafa Fallahpour, Parsa Idehpour, Vignesh Kothapalli +4
Memory enables large language models (LLMs) to use information beyond their parameters, such as past user conversations, long documents, or an agent's earlier actions. For a frozen…
PRISM-Physics: Causal DAG-Based Process Evaluation for Physics Reasoning
Wanjia Zhao, Qinwei Ma, Jingzhe Shi +7
Benchmarks for competition-style reasoning have advanced evaluation in mathematics and programming, yet physics remains comparatively explored. Most existing physics benchmarks eva…
Optimas: Optimizing Compound AI Systems with Globally Aligned Local Rewards
Shirley Wu, Parth Sarthi, Shiyu Zhao +10
Compound AI systems integrating multiple components, such as Large Language Models, specialized tools, and traditional machine learning models, are increasingly deployed to solve c…
AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning
Shirley Wu, Shiyu Zhao, Qian Huang +7
Large language model (LLM) agents have demonstrated impressive capabilities in utilizing external tools and knowledge to boost accuracy and reduce hallucinations. However, developi…
GraphMETRO: Mitigating Complex Graph Distribution Shifts via Mixture of Aligned Experts
Shirley Wu, Kaidi Cao, Bruno Ribeiro +2
Graph data are inherently complex and heterogeneous, leading to a high natural diversity of distributional shifts. However, it remains unclear how to build machine learning archite…
Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
Chenhang Cui, Yiyang Zhou, Xinyu Yang +4
While GPT-4V(ision) impressively models both visual and textual information simultaneously, it's hallucination behavior has not been systematically assessed. To bridge this gap, we…
Communication-Free Distributed GNN Training with Vertex Cut
Kaidi Cao, Rui Deng, Shirley Wu +3
Training Graph Neural Networks (GNNs) on real-world graphs consisting of billions of nodes and edges is quite challenging, primarily due to the substantial memory needed to store t…
Discover and Cure: Concept-aware Mitigation of Spurious Correlation
Shirley Wu, Mert Yuksekgonul, Linjun Zhang +1
Deep neural networks often rely on spurious correlations to make predictions, which hinders generalization beyond training environments. For instance, models that associate cats wi…
Efficient Automatic Machine Learning via Design Graphs
Shirley Wu, Jiaxuan You, Jure Leskovec +1
Despite the success of automated machine learning (AutoML), which aims to find the best design, including the architecture of deep networks and hyper-parameters, conventional AutoM…