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Mingyang Song

Mingyang Song has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Machine translation, Evaluation & benchmarks; often with Mao Zheng, Xuan Luo, Zheng Li.

4 papers in our library · 2025–2026

OpenAlex · ORCID · Wikidata · h-index 83 · 957 works · 28,348 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 56 times in our library · ⭐ Paper Stars ranking: #7637

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 34 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models (2025 · 20 citations)
    Popular long-context tests such as needle-in-a-haystack ask a model to find one inserted sentence. Recent LLMs score almost perfectly on them, so these tests no longer separate strong models from weak ones. The authors propose Counting-Stars, a benchmark that measures how well a model gathers many scattered pieces of…
  2. Hunyuan-MT Technical Report (2025 · 17 citations)
    Open-source LLM translators still trail proprietary systems, and translation between Mandarin and China's minority languages gets little attention.
  3. SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation (2026 · 10 citations)
    Most strong LLM-based machine translation (MT) systems depend on costly external supervision, such as human-made reference translations or trained reward models, and this limits how far they can scale.
  4. Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study (2025 · 9 citations)
    LLM judges are increasingly used to grade other models, but their scores can be unstable and skewed by hidden biases.

 Co-authors here

4 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 56 times (of the 149,439 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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