Hongyu Li
Hongyu Li has 4 papers in our library, in 2020–2026, most of them on Language models & pretraining, Question answering, Text generation; often with Haifeng Wang, Hua Ren Wu, Jing Liu.
OpenAlex · ORCID · Wikidata · h-index 28 · 132 works · 4,174 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 29 times in our library · ⭐ Paper Stars ranking: #9279
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work
🧭 Topics 5Research subjects and languages the work is about.Question answering · 1 workEvaluation & benchmarks · 1 workInformation retrieval & search · 1 workLanguage models & pretraining · 1 workText generation · 1 work
💡 Concepts 1Key ideas and methods explained on our concept pages.Inverted index · 1 work
Works by year
2020s
Papers
- DuReader_retrieval: A Large-scale Chinese Benchmark for Passage Retrieval from Web Search Engine (2022 · 27 citations)
Dense passage retrieval depends on large, human-annotated training and evaluation data. Such resources are plentiful for English but scarce for Chinese, where existing sets are tiny (TianGong-PDR), machine-translated (mMARCO), or built only from click logs (Sougou-QCL). The authors present DuReader_retrieval, a… - DuReader_robust: A Chinese Dataset Towards Evaluating Robustness and Generalization of Machine Reading Comprehension in Real-World Applications (2020 · 13 citations)
Neural machine reading comprehension (MRC) models often fail in real-world use. They may answer paraphrased questions inconsistently (over-sensitivity), get distracted by passage spans that share many words with the question (over-stability), or struggle outside their training domain (generalization). Earlier… - SRCB at SemEval-2026 Task 5 A Multi-Target Finetuning Framework for Large Language Models with Joint Regression and Text Generation (2026 · 1 citation)
The paper describes the first-ranked system for SemEval-2026 Task 5. In this task, models do not pick one correct sense of a homonym. They instead predict how plausible humans find a given sense in a short story, on a 1–5 scale. Evaluation uses Spearman correlation together with an accuracy measure that counts a… - PACE: Predictive Adaptive Context Extraction for Long-Horizon LLM Agents (2026)
LLM agents working on tasks with hundreds or thousands of steps hit a trade-off. Keeping the whole history fills the context window and forces blunt truncation. Summarizing at every step loses details too early. PACE (Lei Wei et al., Alibaba / Peking University, ACL 2026) treats context construction as a Next Step…
Co-authors here
- Haifeng Wang ×2
- Hua Ren Wu ×2
- Jing Liu ×2
- Bin Dong
- Bin Wang
- Qiaoqiao She
- Shanshan Jiang
- Yifu Qiu
- Ying Chen
- Yingqi Qu
- Yu Hong
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2022
- International Workshop on Semantic Evaluation (SemEval): SemEval 2026
Cited by
Updated
This page in Markdown → https://md.research.elamur.ai/free/en/authors/hongyu-li/