50 citations · 51 across the 7 of their papers we have counts for
8 papers
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination
Zhenting Qi, Zhuokai Zhao, Rulin Shao +7
Existing large language model (LLM) based multi-agent systems have been shown to scale poorly, and we identify three problems: reading burden that grows with the group, useful info…
Husky: A Unified, Open-Source Language Agent for Multi-Step Reasoning
Joongwon Kim, Bhargavi Paranjape, Tushar Khot +1
Language agents perform complex tasks by using tools to execute each step precisely. However, most existing agents are based on proprietary models or designed to target specific ta…
Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals
Yanai Elazar, Bhargavi Paranjape, Hao Peng +5
The inevitable appearance of spurious correlations in training datasets hurts the generalization of NLP models on unseen data. Previous work has found that datasets with paired inp…
PuMer: Pruning and Merging Tokens for Efficient Vision Language Models
Qingqing Cao, Bhargavi Paranjape, Hannaneh Hajishirzi
Large-scale vision language (VL) models use Transformers to perform cross-modal interactions between the input text and image. These cross-modal interactions are computationally ex…
ART: Automatic multi-step reasoning and tool-use for large language models
Bhargavi Paranjape, Scott Lundberg, Sameer Singh +3
Large language models (LLMs) can perform complex reasoning in few- and zero-shot settings by generating intermediate chain of thought (CoT) reasoning steps. Further, each reasoning…
AGRO: Adversarial Discovery of Error-prone groups for Robust Optimization
Bhargavi Paranjape, Pradeep Dasigi, Vivek Srikumar +2
Models trained via empirical risk minimization (ERM) are known to rely on spurious correlations between labels and task-independent input features, resulting in poor generalization…
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation
Tanay Dixit, Bhargavi Paranjape, Hannaneh Hajishirzi +1
Counterfactual data augmentation (CDA) -- i.e., adding minimally perturbed inputs during training -- helps reduce model reliance on spurious correlations and improves generalizatio…
Retrieval-guided Counterfactual Generation for QA
Bhargavi Paranjape, Matthew Lamm, Ian Tenney
Deep NLP models have been shown to learn spurious correlations, leaving them brittle to input perturbations. Recent work has shown that counterfactual or contrastive data -- i.e. m…