Mingan Lin
Mingan Lin has 5 papers in our library, in 2024–2025, most of them on Language models & pretraining, Speech & spoken language, Evaluation & benchmarks; often with Haoze Sun, Tao Zhang, Tianpeng Li.
OpenAlex · ORCID · h-index 2 · 10 works · 19 citations OpenAlex, 2026-10-14
📄 5 works in our library · 🔗 Cited 96 times in our library · ⭐ Paper Stars ranking: #7299
Affiliations
Fields, topics and concepts
📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 1 workSignal Processing · 1 work
🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 3 worksSpeech & spoken language · 1 workEvaluation & benchmarks · 1 workVision & language · 1 work
Works by year
2020s
Papers
- CFBench: A Comprehensive Constraints-Following Benchmark for LLMs (2025 · 30 citations)
CFBench tackles a gap in how LLMs are tested on following instructions that carry many explicit constraints. - Baichuan-Omni Technical Report (2024 · 18 citations)
GPT-4o showed what a single model handling several input types can do, but the open-source community had no comparable system. - Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction (2025 · 16 citations)
Cascaded voice assistants chain ASR, an LLM and TTS. This adds latency, compounds errors and discards paralinguistic cues. End-to-end alternatives such as Moshi and GLM-4-Voice tend to lose reasoning ability once speech is added. Baichuan-Audio is a 7B end-to-end audio LLM for real-time bilingual (Chinese/English)… - Baichuan Alignment Technical Report (2024 · 5 citations)
Alignment methods for large language models are usually kept proprietary or described only in fragments. This technical report from Baichuan Inc. and Peking University sets out the full alignment pipeline behind the Baichuan models. The pipeline has three stages. A Prompt Augmentation System (PAS) automatically… - BaichuanSEED: Sharing the Potential of ExtensivE Data Collection and Deduplication by Introducing a Competitive Large Language Model Baseline (2024 · 2 citations)
Pre-training corpora largely determine what a large language model can do. Yet many developers, including some that release open weights, keep their data collection and mixing recipes private. Some models are also tuned toward particular benchmarks, which hides their real abilities.
Co-authors here
- Haoze Sun ×4
- Tao Zhang ×4
- Tianpeng Li ×4
- Fan Yang ×3
- Guosheng Dong ×3
- Yanjun Shen ×3
- Da Pan ×2
- Jianhua Xu ×2
- Xu Li ×2
- Zheng Liang ×2
- Bin Cui
- Bowen Ding
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