Papers by Ru Yang
GLoCIM: Global-view Long Chain Interest Modeling for news recommendation (2025.coling-main)
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| Challenge: | Recent efforts to extract local subgraph information from click graphs have hindered collaboratively utilizing global click graph information. |
| Approach: | They propose a global-view long chain interests model that models a click graph with neighbor interest to enhance news recommendation. |
| Outcome: | The proposed method surpasses baseline methods on two real-world datasets. |
DAGCN: Distance-based and Aspect-oriented Graph Convolutional Network for Aspect-based Sentiment Analysis (2024.findings-naacl)
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| Challenge: | Recent advances in sentiment analysis tend to interference from local factors such as irrelevant words and edges, hindering the precise identification of opinion words. |
| Approach: | They propose a distance-based syntactic weight and Aspect-Fusion Attention to solve this problem. |
| Outcome: | The proposed model outperforms state-of-the-art models on three public datasets and verify its effectiveness. |
Knowledge-Centric Hallucination Detection (2024.emnlp-main)
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Xiangkun Hu, Dongyu Ru, Lin Qiu, Qipeng Guo, Tianhang Zhang, Yang Xu, Yun Luo, Pengfei Liu, Yue Zhang, Zheng Zhang
| Challenge: | Large Language Models (LLMs) have shown impressive capabilities but a tendency to hallucinate. |
| Approach: | They propose a framework that introduces claim-triplets to represent claims in LLM responses and evaluates them against a reference. |
| Outcome: | The proposed framework outperforms prior methods by 18.2 to 27.2 points on a benchmark spanning various NLP tasks and annotated 11k claim-triplets from 2.1k responses by seven LLMs. |
OpenResearcher: Unleashing AI for Accelerated Scientific Research (2024.emnlp-demo)
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Yuxiang Zheng, Shichao Sun, Lin Qiu, Dongyu Ru, Cheng Jiayang, Xuefeng Li, Jifan Lin, Binjie Wang, Yun Luo, Renjie Pan, Yang Xu, Qingkai Min, Zizhao Zhang, Yiwen Wang, Wenjie Li, Pengfei Liu
| Challenge: | Global scientific publications are growing annually by about 4%-5% (Pinedo et al., 2024). |
| Approach: | They introduce an AI-assisted platform that answers diverse questions from researchers using Retrieval-Augmented Generation (RAG) they develop various tools to understand queries, search from the scientific literature, filter retrieved information, provide accurate and comprehensive answers, and self-refine answers. |
| Outcome: | OpenResearcher is built on Retrieval-Augmented Generation (RAG) to integrate Large Language Models (LLMs) with up-to-date, domain-specific knowledge. |
ATRI: Mitigating Multilingual Audio Text Retrieval Inconsistencies by Reducing Data Distribution Errors (2025.acl-long)
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Yuguo Yin, Yuxin Xie, Wenyuan Yang, Dongchao Yang, Jinghan Ru, Xianwei Zhuang, Liming Liang, Yuexian Zou
| Challenge: | Existing multilingual audio-text retrieval schemes suffer from inconsistencies for instance similarity matching across languages. |
| Approach: | They propose a multilingual audio-text retrieval scheme that mitigates the impact of data distribution error on recall and consistency. |
| Outcome: | The proposed scheme achieves state-of-the-art performance on recall and consistency metrics for eight mainstream languages, including English. |
HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)
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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao
| Challenge: | High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly. |
| Approach: | They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth. |
| Outcome: | the proposed pipeline outperforms 14 leading baselines on 16 benchmarks. |