7 papers
Evian: Towards Explainable Visual Instruction-tuning Data Auditing
Zimu Jia, Mingjie Xu, Andrew Estornell +1
The efficacy of Large Vision-Language Models (LVLMs) is critically dependent on the quality of their training data, requiring a precise balance between visual fidelity and instruct…
AgenticMath: Enhancing LLM Reasoning via Agentic-based Math Data Generation
Xianyang Liu, Yilin Liu, Shuai Wang +5
The creation of high-quality datasets to improve Large Language Model (LLM) reasoning remains a significant challenge, as current methods often suffer from generating low-quality/i…
Any-Depth Alignment: Unlocking Innate Safety Alignment of LLMs to Any-Depth
Jiawei Zhang, Andrew Estornell, David D. Baek +2
Large Language Models (LLMs) exhibit strong but shallow alignment: they directly refuse harmful queries when a refusal is expected at the very start of an assistant turn, yet this…
How to Train a Leader: Hierarchical Reasoning in Multi-Agent LLMs
Andrew Estornell, Jean-Francois Ton, Muhammad Faaiz Taufiq +1
Large Language Models (LLMs) have achieved strong performance on a wide range of complex reasoning tasks, yet further gains are often possible by leveraging the complementary stren…
Better Reasoning with Less Data: Enhancing VLMs Through Unified Modality Scoring
Mingjie Xu, Andrew Estornell, Hongzheng Yang +4
The application of visual instruction tuning and other post-training techniques has significantly enhanced the capabilities of Large Language Models (LLMs) in visual understanding,…
To Give or Not to Give? The Impacts of Strategically Withheld Recourse
Yatong Chen, Andrew Estornell, Yevgeniy Vorobeychik +1
Individuals often aim to reverse undesired outcomes in interactions with automated systems, like loan denials, by either implementing system-recommended actions (recourse), or mani…