Papers by Chenghao Jia
SynET: Synonym Expansion using Transitivity (2020.findings-emnlp)
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| Challenge: | Existing approaches to find synonyms from text corpora are distributed and pattern based, but they suffer from low precision and low recall. |
| Approach: | They propose a task of synonym expansion using transitivity and propose auxiliary task to reduce the impact of noisy sentences. |
| Outcome: | The proposed approach reduces the impact of noisy sentences and reduces noise in a real-world dataset. |
Heterogeneous Graph Neural Networks for Concept Prerequisite Relation Learning in Educational Data (2021.naacl-main)
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| Challenge: | Existing methods to learn prerequisite relations among concepts are lacking . concepts are crucial for learning, organizing, applying and generating knowledge . |
| Approach: | They propose a concept prerequisite relation learning approach which combines concept representation and concept pairwise features to make it more practical. |
| Outcome: | The proposed method achieves state-of-the-art results on four datasets. |
LSDC: An Efficient and Effective Large-Scale Data Compression Method for Supervised Fine-tuning of Large Language Models (2025.findings-naacl)
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Zhaoguang Long, Yuhao Zhou, Shangqing Zhao, Yupei Ren, Li Cai, Chenghao Jia, Zhe Chen, Zhe Fang, Yuxiang Song, Man Lan
| Challenge: | Large Language Models (LLMs) are expanding in scale and size, increasing computational costs . large-scale data compression techniques can reduce the size of training datasets while maintaining data integrity. |
| Approach: | They propose a large-scale data compression method to reduce the size of training data . they use a bifurcated quantization strategy to maximize the diversity of samples . |
| Outcome: | The proposed method significantly reduces the size of training data while maximizing the submodular gain. |
Can Intelligent Agents Revolutionize Scale Generation? (2026.findings-acl)
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| Challenge: | Existing measurement scales require extensive manual labor and require extensive validation and validation. |
| Approach: | They propose a multi-agent framework that automates scale development by leveraging collaborative AI agents. |
| Outcome: | The proposed framework automates scale development while maintaining rigorous quality standards. |
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)
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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain. |
| Approach: | FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. |
| Outcome: | FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability. |
Adversarial Self-Supervised Data-Free Distillation for Text Classification (2020.emnlp-main)
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| Challenge: | Existing knowledge distillation algorithms rely on the accessibility of the training dataset, which may be unavailable due to privacy issues. |
| Approach: | They propose a data-free distillation method for a pre-trained transformer-based model that uses plug & play Embedding Guessing to craft pseudo embeddings from the teacher's hidden knowledge. |
| Outcome: | The proposed method is the first data-free distillation framework designed for NLP tasks. |