Papers by Ziyuan Yang
SPORTSINTERVIEW: A Large-Scale Sports Interview Benchmark for Entity-centric Dialogues (2022.lrec-1)
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| Challenge: | Existing knowledge grounded dialogue datasets only contain external knowledge from one dimension, which limits the diversity of knowledge sources and may contain unwanted bias. |
| Approach: | They propose to use two types of external knowledge sources as knowledge grounding in an interview dataset to model human dialogues. |
| Outcome: | The proposed dataset contains 150K interviews and 34K interviewees . it is larger in size and has more than one dimension of external knowledge linking . however, the performance of the proposed models is far from humans . |
Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments (2024.findings-acl)
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Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, Qi Zhang
| Challenge: | Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. |
| Approach: | They propose a framework that allows LLMs to efficiently and faithfully reason over structured environments. |
| Outcome: | The proposed framework surpasses state-of-the-art fine-tuned methods on three KGQA and two TableQA datasets and surpasse CWQ and WTQ methods. |
GigaSpeech 2: An Evolving, Large-Scale and Multi-domain ASR Corpus for Low-Resource Languages with Automated Crawling, Transcription and Refinement (2025.acl-long)
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Yifan Yang, Zheshu Song, Jianheng Zhuo, Mingyu Cui, Jinpeng Li, Bo Yang, Yexing Du, Ziyang Ma, Xunying Liu, Ziyuan Wang, Ke Li, Shuai Fan, Kai Yu, Wei-Qiang Zhang, Guoguo Chen, Xie Chen
| Challenge: | GigaSpeech 2 is a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages. |
| Approach: | They propose a large-scale, multi-domain, multilingual speech recognition corpus for low-resource languages and an automated pipeline for data crawling, transcription, and label refinement. |
| Outcome: | The proposed corpus reduces the word error rate for Thai, Indonesian, and Vietnamese on a realistic YouTube test set by 25% to 40% compared to Whisper large-v3. |
EfficientRAG: Efficient Retriever for Multi-Hop Question Answering (2024.emnlp-main)
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Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang
| Challenge: | Existing retrieval-augmented generation methods rely on multiple calls of large language models (LLMs) Large-language models lack knowledge underrepresented in training data and still face hallucinations. |
| Approach: | They propose an efficient retriever for multi-hop question answering that generates new queries iteratively without the need for LLM calls. |
| Outcome: | The proposed method surpasses existing methods on three open-domain multi-hop question-answering datasets. |
Among Us: Measuring and Mitigating Malicious Contributions in Model Collaboration Systems (2026.acl-long)
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| Challenge: | Existing research is leveraging multiple language models with diverse skills and strengths to collaborate. |
| Approach: | They propose mitigation strategies to mitigate the impact of malicious models by employing external supervisors to disable/mask them out to reduce their influence. |
| Outcome: | The proposed mitigation strategies recover 95.31% of initial performance while making model collaboration systems fully resistant to malicious models remains an open question. |