10 citations · 15 across the 9 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…
Realize What Matters: Principled Context Representation for Large-Scale Reasoning
Michael Theologitis, Dean Light, Shuyue Stella Li +3
Solving complex tasks in domains such as science, medicine, law, and finance often requires assembling interdependent information scattered across vast, heterogeneous sources far b…
Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
Dean Light, Michael Theologitis, Kshitish Ghate +7
Humans intuitively solve complex problems by flexibly shifting among reasoning modes: they plan, execute, revise intermediate goals, resolve ambiguity through associative judgment,…
Synthetic Mixed Training: Scaling Parametric Knowledge Acquisition Beyond RAG
Seungju Han, Konwoo Kim, Chanwoo Park +5
Synthetic data augmentation helps language models learn new knowledge in data-constrained domains. However, naively scaling existing synthetic data methods by training on more synt…
FDARxBench: Benchmarking Regulatory and Clinical Reasoning on FDA Generic Drug Assessment
Betty Xiong, Jillian Fisher, Benjamin Newman +5
We introduce an expert curated, real-world benchmark for evaluating document-grounded question-answering (QA) motivated by generic drug assessment, using the U.S. Food and Drug Adm…
Spectrum Tuning: Post-Training for Distributional Coverage and In-Context Steerability
Taylor Sorensen, Benjamin Newman, Jared Moore +5
Language model post-training has enhanced instruction-following and performance on many downstream tasks, but also comes with an often-overlooked cost on tasks with many possible v…
The Curious Case of Factuality Finetuning: Models' Internal Beliefs Can Improve Factuality
Benjamin Newman, Abhilasha Ravichander, Jaehun Jung +5
Language models are prone to hallucination - generating text that is factually incorrect. Finetuning models on high-quality factual information can potentially reduce hallucination…
Verifying the Verifiers: Unveiling Pitfalls and Potentials in Fact Verifiers
Wooseok Seo, Seungju Han, Jaehun Jung +6
Fact verification is essential for ensuring the reliability of LLM applications. In this study, we evaluate 12 pre-trained LLMs and one specialized fact-verifier, including frontie…
WildHallucinations: Evaluating Long-form Factuality in LLMs with Real-World Entity Queries
Wenting Zhao, Tanya Goyal, Yu Ying Chiu +8
While hallucinations of large language models (LLMs) prevail as a major challenge, existing evaluation benchmarks on factuality do not cover the diverse domains of knowledge that t…
The Generative AI Paradox: "What It Can Create, It May Not Understand"
Peter West, Ximing Lu, Nouha Dziri +11
The recent wave of generative AI has sparked unprecedented global attention, with both excitement and concern over potentially superhuman levels of artificial intelligence: models…