Jing Xiong
Jing Xiong has 4 papers in our library, in 2023–2026, most of them on Reasoning, Language models & pretraining, Information retrieval & search; often with Chengming Li, Zhijiang Guo, Bin Hu.
OpenAlex · ORCID · h-index 17 · 172 works · 1,004 citations OpenAlex, 2026-10-14
📄 4 works in our library · 🔗 Cited 128 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7372
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 worksComputational Theory and Mathematics · 2 worksInformation Systems · 1 work
🧭 Topics 5Research subjects and languages the work is about.Reasoning · 2 worksSyntax & grammar formalisms · 1 workEfficient models & compression · 1 workInformation retrieval & search · 1 workLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning (2023 · 27 citations)
The order and choice of demonstrations in a prompt strongly affect how well large language models solve multi-step reasoning problems. - FormalAlign: Automated Alignment Evaluation for Autoformalization (2024 · 18 citations)
Autoformalization uses models to turn informal mathematical statements into formal code such as Lean 4. A key weakness is evaluation. Compilers only confirm that the output is valid code, and BLEU or BERTscore only measure surface or embedding similarity. Neither checks whether the formal statement actually means the… - Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation (2026 · 13 citations)
Small LLMs are usually distilled by fine-tuning them on outputs from the most powerful available teacher. Prior work shows this can backfire: very strong teachers may produce answers that are too complex or too far from what the student can absorb. Earlier remedies (Mix, CAR) still make every candidate teacher answer… - Expression Syntax Information Bottleneck for Math Word Problems (2023 · 10 citations)
Neural solvers for math word problems (MWP) often score well by exploiting shallow links between wording and answer templates.
All works
- LEGO-Prover: Neural Theorem Proving with Growing Libraries ↗arXiv:2310.00656 · no summary yetHaiming Wang et al.2023 · 79 citations
- DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningarXiv:2310.02954Jing Xiong, Zixuan Li, Chuanyang Zheng, et al.2023 · 27 citations
- FormalAlign: Automated Alignment Evaluation for AutoformalizationarXiv:2410.10135Jianqiao Lu, Yingjia Wan, Yinya Huang, et al.2024 · 18 citations
- Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher DistillationAnnual Meeting of the Association for Computational Linguistics (ACL)Hengyuan Zhang, Shiping Yang, Xiao Liang, et al.2026 · 13 citations
- Expression Syntax Information Bottleneck for Math Word ProblemsInternational ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)Jing Xiong, Chengming Li, Min Yang, et al.2023 · 10 citations
Co-authors here
- Chengming Li ×2
- Zhijiang Guo ×2
- Bin Hu
- Chaofan Tao
- Chenming Shang
- Hayden Kwok-Hay So
- Hengyuan Zhang
- Jianqiao Lu
- Jing Tang
- Min Yang
- Ngai Wong
- Ruobing Xie
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR): SIGIR 2023
Cited by
Updated
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