1 citations · 1 across the 12 of their papers we have counts for
12 papers
Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents
Kaituo Zhang, Zhen Xiong, Mingyu Zhong +4
Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this conse…
Generalist Scanner Meets Specialist Locator: A Synergistic Coarse-to-Fine Framework for Robust GUI Grounding
Zhecheng Li, Guoxian Song, Yiwei Wang +3
Grounding natural language queries in graphical user interfaces (GUIs) presents a challenging task that requires models to comprehend diverse UI elements across various application…
Thinking with Sound: Audio Chain-of-Thought Enables Multimodal Reasoning in Large Audio-Language Models
Zhen Xiong, Yujun Cai, Zhecheng Li +2
Recent Large Audio-Language Models (LALMs) have shown strong performance on various audio understanding tasks such as speech translation and Audio Q\&A. However, they exhibit signi…
: Advancing Img2LaTeX Conversion via Visual Reasoning with Attention-Guided Refinement
Zhecheng Li, Guoxian Song, Yiwei Wang +3
Img2LaTeX is a practically important task that involves translating mathematical expressions and structured visual content from images into LaTeX code. In recent years, vision-lang…
Unveiling the Potential of Diffusion Large Language Model in Controllable Generation
Zhen Xiong, Yujun Cai, Zhecheng Li +1
Controllable generation is a fundamental task in NLP with many applications, providing a basis for function calling to agentic communication. However, even state-of-the-art autoreg…
Who is in the Spotlight: The Hidden Bias Undermining Multimodal Retrieval-Augmented Generation
Jiayu Yao, Shenghua Liu, Yiwei Wang +5
Multimodal Retrieval-Augmented Generation (RAG) systems have become essential in knowledge-intensive and open-domain tasks. As retrieval complexity increases, ensuring the robustne…
Mapping the Minds of LLMs: A Graph-Based Analysis of Reasoning LLM
Zhen Xiong, Yujun Cai, Zhecheng Li +1
Recent advances in test-time scaling have enabled Large Language Models (LLMs) to display sophisticated reasoning abilities via extended Chain-of-Thought (CoT) generation. Despite…
Enhancing LLM Character-Level Manipulation via Divide and Conquer
Zhen Xiong, Yujun Cai, Bryan Hooi +3
Large Language Models (LLMs) have demonstrated strong generalization capabilities across a wide range of natural language processing (NLP) tasks. However, they exhibit notable weak…
Texture or Semantics? Vision-Language Models Get Lost in Font Recognition
Zhecheng Li, Guoxian Song, Yujun Cai +3
Modern Vision-Language Models (VLMs) exhibit remarkable visual and linguistic capabilities, achieving impressive performance in various tasks such as image recognition and object l…
DRS: Deep Question Reformulation With Structured Output
Zhecheng Li, Yiwei Wang, Bryan Hooi +3
Question answering represents a core capability of large language models (LLMs). However, when individuals encounter unfamiliar knowledge in texts, they often formulate questions t…
Think Carefully and Check Again! Meta-Generation Unlocking LLMs for Low-Resource Cross-Lingual Summarization
Zhecheng Li, Yiwei Wang, Bryan Hooi +4
Cross-lingual summarization (CLS) aims to generate a summary for the source text in a different target language. Currently, instruction-tuned large language models (LLMs) excel at…
Vulnerability of LLMs to Vertically Aligned Text Manipulations
Zhecheng Li, Yiwei Wang, Bryan Hooi +4
Vertical text input is commonly encountered in various real-world applications, such as mathematical computations and word-based Sudoku puzzles. While current large language models…