Boyuan Pan
Boyuan Pan has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Kan Li, Peiwen Yuan, Shaoxiong Feng.
OpenAlex · h-index 8 · 51 works · 273 citations OpenAlex, 2026-10-14
📄 2 works in our library · 🔗 Cited 82 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9409
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work
🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 work
Works by year
2020s
Papers
- Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step Reasoning (2024 · 57 citations)
Self-consistency (SC) improves chain-of-thought reasoning by sampling many reasoning paths and taking a majority vote. - LLM-Powered Benchmark Factory: Reliable, Generic, and Efficient (2026 · 3 citations)
With tens of thousands of new LLMs appearing each season, practitioners need custom benchmarks to choose models for their tasks.
All works
- T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetBoxin Wang et al.2020 · 82 citations
- Escape Sky-high Cost: Early-stopping Self-Consistency for Multi-step ReasoningarXiv:2401.10480Yiwei Li, Peiwen Yuan, Shaoxiong Feng, et al.2024 · 57 citations
- LLM-Powered Benchmark Factory: Reliable, Generic, and EfficientAnnual Meeting of the Association for Computational Linguistics (ACL)Peiwen Yuan, Shaoxiong Feng, Yiwei Li, et al.2026 · 3 citations
Co-authors here
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2026
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Cited by
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