10 citations · 12 across the 11 of their papers we have counts for
11 papers · 1 filter
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…
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…