Han Wang
Han Wang has 6 papers in our library, in 2018–2026, most of them on Reasoning, Language models & pretraining, Speech & spoken language; often with Changhao Pan, Chenyuhao Wen, Haiyang Yu.
OpenAlex · ORCID · h-index 30 · 187 works · 3,225 citations OpenAlex, 2026-10-14
📄 6 works in our library · 🔗 Cited 67 times in our library · ⭐ Paper Stars ranking: #7015
Affiliations
Fields, topics and concepts
📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 6 worksComputer Science · 6 worksArtificial Intelligence · 6 worksLanguage and Linguistics · 6 worksComputer Vision and Pattern Recognition · 2 works
🧭 Topics 7Research subjects and languages the work is about.Reasoning · 3 worksLanguage models & pretraining · 2 worksBias, fairness & safety · 1 workEfficient models & compression · 1 workEnglish · 1 workEvaluation & benchmarks · 1 workSpeech & spoken language · 1 work
Works by year
2020s
2010s
Papers
- Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024 · 63 citations)
When an instruction-tuned LLM is further fine-tuned for a narrow downstream task, it tends to lose general abilities such as instruction following, helpfulness and safety alignment. - Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning (2025 · 11 citations)
Reasoning-tuned LLMs and MLLMs tend to produce long chain-of-thought output for every query. This costs many tokens and can even hurt accuracy on easy, fact-based questions. A pilot with Qwen2.5-0.5B on text-rich VQA and KIE data showed that direct answers were both more accurate and far cheaper than reasoning-based… - AlphaOne: Reasoning Models Thinking Slow and Fast at Test Time (2025 · 10 citations)
Models like OpenAI o1 and DeepSeek-R1, known as large reasoning models (LRMs), frequently overthink or underthink at inference time. - English Out-of-Vocabulary Lexical Evaluation Task (2018 · 3 citations)
The paper proposes a benchmark for words a model has never seen: out-of-vocabulary (OOV) items that occur only in test material, so their meaning must come from surrounding text. - Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026)
Long-form text-to-speech has shifted from single sentences to paragraphs and multi-speaker dialogue, but evaluation has not kept pace. - Stabilizing Efficient Reasoning with Step-Level Advantage Selection (2026)
Methods that make LLM reasoning shorter usually add length penalties or pruning. They also tend to post-train in a much smaller context window than the base model used. The authors separate these two factors. They post-train DeepScaleR-1.5B-Preview with plain GRPO in a 4K window, using only a correctness reward. This…
Co-authors here
- Changhao Pan
- Chenyuhao Wen
- Haiyang Yu
- Hao Feng
- Jialian Wu
- Jiang Liu
- Jinghui Lu
- Jingqun Tang
- Jitendra Malik
- Mohit Bansal
- Qian Liu
- Rui Yang
Venues
- Findings of the Association for Computational Linguistics ×2: Findings of the Association for Computational Linguistics 2026 ×2
- Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2024
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
Cited by
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