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Parsa Hosseini

3 accepted papers

2026

GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs

ICLR 2026poster

Object hallucination in Multimodal Large Language Models (MLLMs) is a persistent failure mode that causes the model to perceive objects absent in the image. This weakness of MLLMs is currently studied using static benchmarks with fixed visual scenarios, which preempts the possibility of uncovering m…

Cited by 0SourcecodeScholar
2025

Tool Preferences in Agentic LLMs are Unreliable

EMNLP 2025

Large language models (LLMs) can now access a wide range of external tools, thanks to the Model Context Protocol (MCP). This greatly expands their abilities as various agents. However, LLMs rely entirely on the text descriptions of tools to decide which ones to use—a process that is surprisingly fra

2024

Decompose-and-Compose: A Compositional Approach to Mitigating Spurious Correlation

CVPR 2024poster

While standard Empirical Risk Minimization (ERM) training is proven effective for image classification on in-distribution data it fails to perform well on out-of-distribution samples. One of the main sources of distribution shift for image classification is the compositional nature of images. Specif…