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Zhiming Ding

7 accepted papers

2026

DeepPhy: Benchmarking Agentic VLMs on Physical Reasoning

AAAI 2026technical

Although Vision Language Models (VLMs) exhibit strong perceptual abilities and impressive visual reasoning, they struggle with attention to detail and precise action planning in complex, dynamic environments, leading to subpar performance. Real-world tasks typically require complex interactions, adv

Cited by 0SourcePDFScholar
2025

Extract Information from Hybrid Long Documents Leveraging LLMs: A Framework and Dataset

ICASSP 2025accepted

Large Language Models (LLMs) demonstrate exceptional performance in textual understanding and tabular reasoning tasks. However, their ability to comprehend and analyze hybrid text, containing textual and tabular data, remains unexplored. The hybrid text often appears in the form of hybrid long docum…

Cited by 0SourceScholar
2025

MindRef: Mimicking Human Memory for Hierarchical Reference Retrieval with Fine-Grained Location Awareness

ACL 2025short

When completing knowledge-intensive tasks, humans sometimes need an answer and a corresponding reference passage for auxiliary reading. Previous methods required obtaining pre-segmented article chunks through additional retrieval models. This paper explores leveraging the parameterized knowledge sto…

2025

Vulnerability of Text-to-Image Models to Prompt Template Stealing: A Differential Evolution Approach

ACL 2025finding

Prompt trading has emerged as a significant intellectual property concern in recent years, where vendors entice users by showcasing sample images before selling prompt templates that can generate similar images. This work investigates a critical security vulnerability: attackers can steal prompt tem…

2024

AMPO: Automatic Multi-Branched Prompt Optimization

EMNLP 2024main

Prompt engineering is very important to enhance the performance of large language models (LLMs). When dealing with complex issues, prompt engineers tend to distill multiple patterns from examples and inject relevant solutions to optimize the prompts, achieving satisfying results. However, existing a…

Cited by 3SourcePDFScholar
2024

StraGo: Harnessing Strategic Guidance for Prompt Optimization

EMNLP 2024finding

Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, wherein newly generated prompts canadversely impact previously succe…