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Lin Lee Cheong

4 accepted papers

2026

Collaborative LLM Numerical Reasoning with Local Data Protection

AAAI 2026technical

Numerical reasoning over documents, which demands both contextual understanding and logical inference, is challenging for low-capacity local models deployed on computation-constrained devices. Although such complex reasoning queries could be routed to powerful remote models like GPT-4, exposing loca

Cited by 0SourcePDFScholar
2025

A Systematic Survey of Automatic Prompt Optimization Techniques

EMNLP 2025

Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. However, prompt engineering remains an impediment for end users due to rapid advances in models, tasks, and associated bes

Cited by 0SourcePDFScholar
2025

Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models

NAACL 2025short

Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability. Surprisingly, we find that simple object-based visual prompting—overlaying visual cues (e.g., bounding box, circle) on images—can significantly mitigate such hallucination; however, diffe…

Cited by 0SourcePDFScholar
2021

Single View Physical Distance Estimation Using Human Pose

ICCV 2021poster

We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video captured by a camera viewing a 3-D scene from a fixed vantage point. To automate camera calibration and distance estimatio…

Cited by 11PDFScholar