19 citations · 21 across the 8 of their papers we have counts for
10 papers
World of SWECraft: Agent Collaboration at Scale via Simulated Software Worlds
Shannon Zejiang Shen, Yapei Chang, Qiyao Liang +13
As teams of AI agents take on larger projects and long-standing open problems, agent collaboration offers another dimension for scaling inference-time compute. To understand what m…
Align AI to Dynamic Human-AI Workflows
Valerie Chen, Cleotilde Gonzalez, Anita Williams Woolley +4
Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature…
SpeechEQ: Benchmarking Emotional Intelligence Quotient in Socially Aware Voice Conversational Models
Liang-Yuan Wu, Zih-Ching Chen, Tongshuang Wu +2
As multimodal conversational systems increasingly engage in spoken interaction, their ability to navigate paralinguistic social cues has become a critical bottleneck for natural hu…
Discretizing Reward Models
Vijay Viswanathan, Shiqi Wang, Devamanyu Hazarika +4
Despite their widespread use, the role of reward models in shaping reinforcement learning is poorly understood. Reward models offer a tempting promise: they automatically estimate…
Checklists Are Better Than Reward Models For Aligning Language Models
Vijay Viswanathan, Yanchao Sun, Shuang Ma +4
Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this -- typically using fixed criteria such as "help…
SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning
Chenyang Zhao, Xueying Jia, Vijay Viswanathan +2
Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. However, prompting often leads models to make predic…
Synthetic Multimodal Question Generation
Ian Wu, Sravan Jayanthi, Vijay Viswanathan +4
Multimodal Retrieval Augmented Generation (MMRAG) is a powerful approach to question-answering over multimodal documents. A key challenge with evaluating MMRAG is the paucity of hi…
Better Synthetic Data by Retrieving and Transforming Existing Datasets
Saumya Gandhi, Ritu Gala, Vijay Viswanathan +2
Despite recent advances in large language models, building dependable and deployable NLP models typically requires abundant, high-quality training data. However, task-specific data…
Wikibench: Community-Driven Data Curation for AI Evaluation on Wikipedia
Tzu-Sheng Kuo, Aaron Halfaker, Zirui Cheng +5
AI tools are increasingly deployed in community contexts. However, datasets used to evaluate AI are typically created by developers and annotators outside a given community, which…
Measuring Adversarial Datasets
Yuanchen Bai, Raoyi Huang, Vijay Viswanathan +2
In the era of widespread public use of AI systems across various domains, ensuring adversarial robustness has become increasingly vital to maintain safety and prevent undesirable e…