10 citations · 12 across the 11 of their papers we have counts for
14 papers
Why Generate a Click? Plug-in Decision Heads for Web Agents
Chengguang Gan, Yunhao Liang, Hanjun Wei +3
Web agents built on vision language models decide every action by generating text such as click(12), although most of that text encodes a choice among a few action types and a few…
Certified Selective Automation of LLM Agent Evaluation
Chengguang Gan, Yunhao Liang, Qinghao Zhang +1
Evaluating LLM agents still ends with a human reading trajectories, because automatic judges carry no guarantee on how often they are wrong. We ask the operational question: what f…
How Output Format Confounds Data Quality and Capability in Instruction Tuning
Chengguang Gan, Hanjun Wei, Yunhao Liang +3
Instruction-tuning data are judged by quality metrics, and tuned models are judged by benchmarks, but both judgments pass through an output interface: the surface format in which a…
Joint Training Is Not Enough: Conditioned Cross-Granularity Training for Multimodal Document Understanding
Chengguang Gan, Yunhao Liang, Hanjun Wei +2
The Mutual Reinforcement Effect (MRE) asks whether a fine, span-level and a coarse, document-level task help each other when one model handles both. We test it in multimodal docume…
MAG: A Web-Agent Benchmark and Harness for Multimodal Action and Guide Generation
Chengguang Gan, Hanjun Wei, Yunhao Liang +3
Digital Adoption Platforms (DAPs) are embedded overlays widely used on web systems to guide users through operations inside a page, helping them get started with unfamiliar interfa…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
Chengguang Gan, Zhixi Cai, Yunhao Liang +3
Reinforcement learning with verifiable rewards, and Group Relative Policy Optimization (GRPO) in particular, is now run routinely on a supervised checkpoint in the hope of producin…
Interpretable Clustering with Adaptive Heterogeneous Causal Structure Learning in Mixed Observational Data
Wenrui Li, Qinghao Zhang, Xiaowo Wang
Understanding causal heterogeneity is essential for scientific discovery in domains such as biology and medicine. However, existing methods lack causal awareness, with insufficient…
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction
Chengguang Gan, Sunbowen Lee, Qingyu Yin +9
The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modele…
Demonstrating Mutual Reinforcement Effect through Information Flow
Chengguang Gan, Xuzheng He, Qinghao Zhang +1
The Mutual Reinforcement Effect (MRE) investigates the synergistic relationship between word-level and text-level classifications in text classification tasks. It posits that the p…
Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language p…
Think from Words(TFW): Initiating Human-Like Cognition in Large Language Models Through Think from Words for Japanese Text-level Classification
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
The proliferation of Large Language Models (LLMs) has spurred extensive research into LLM-related Prompt investigations, such as Instruction Learning (IL), In-context Learning (ICL…
GIELLM: Japanese General Information Extraction Large Language Model Utilizing Mutual Reinforcement Effect
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
Information Extraction (IE) stands as a cornerstone in natural language processing, traditionally segmented into distinct sub-tasks. The advent of Large Language Models (LLMs) hera…
USA: Universal Sentiment Analysis Model & Construction of Japanese Sentiment Text Classification and Part of Speech Dataset
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
Sentiment analysis is a pivotal task in the domain of natural language processing. It encompasses both text-level sentiment polarity classification and word-level Part of Speech(PO…
Mutual Reinforcement Effects in Japanese Sentence Classification and Named Entity Recognition Tasks
Chengguang Gan, Qinghao Zhang, Tatsunori Mori
Information extraction(IE) is a crucial subfield within natural language processing. However, for the traditionally segmented approach to sentence classification and Named Entity R…