1 citations · 3 across the 12 of their papers we have counts for
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Tools Fail: Detecting Silent Errors in Faulty Tools
Jimin Sun, So Yeon Min, Yingshan Chang +1
Tools have become a mainstay of LLMs, allowing them to retrieve knowledge not in their weights, to perform tasks on the web, and even to control robots. However, most ontologies an…
DiffusionPID: Interpreting Diffusion via Partial Information Decomposition
Rushikesh Zawar, Shaurya Dewan, Prakanshul Saxena +3
Text-to-image diffusion models have made significant progress in generating naturalistic images from textual inputs, and demonstrate the capacity to learn and represent complex vis…
Language Models Need Inductive Biases to Count Inductively
Yingshan Chang, Yonatan Bisk
Counting is a fundamental example of generalization, whether viewed through the mathematical lens of Peano's axioms defining the natural numbers or the cognitive science literature…
Flow Priors for Linear Inverse Problems via Iterative Corrupted Trajectory Matching
Yasi Zhang, Peiyu Yu, Yaxuan Zhu +4
Generative models based on flow matching have attracted significant attention for their simplicity and superior performance in high-resolution image synthesis. By leveraging the in…
Skews in the Phenomenon Space Hinder Generalization in Text-to-Image Generation
Yingshan Chang, Yasi Zhang, Zhiyuan Fang +3
The literature on text-to-image generation is plagued by issues of faithfully composing entities with relations. But there lacks a formal understanding of how entity-relation compo…
VISREAS: Complex Visual Reasoning with Unanswerable Questions
Syeda Nahida Akter, Sangwu Lee, Yingshan Chang +2
Verifying a question's validity before answering is crucial in real-world applications, where users may provide imperfect instructions. In this scenario, an ideal model should addr…