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Minghao Wu

Minghao Wu has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Machine translation, Speech & spoken language; often with Alham Fikri Aji, Chenyang Lyu, Longyue Wang.

4 papers in our library · 2023–2026

OpenAlex · ORCID · h-index 19 · 119 works · 1,000 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 354 times in our library · 5 more in our library without a page yet · ⭐ Paper Star rank #4371

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. 20 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank Adaptation (2023 · 95 citations)
    Open instruction-tuned LLMs have mostly been trained on English data, and existing multilingual resources such as xP3/xP3mt depend on fixed templates and classic NLP tasks rather than open-ended requests.
  2. Style Over Substance: Evaluation Biases for Large Language Models (2025 · 77 citations)
    Pairwise comparisons scored with Elo ratings are a common way to rank LLMs on open-ended tasks. This approach assumes that the judges, whether people or LLMs, can reliably spot the better answer. Wu and Aji test that assumption with deliberately degraded responses. They took 40 non-specialist questions from the…
  3. Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration (2023 · 74 citations)
    Instruction-tuned LLMs are strong on text, but how well they handle other kinds of input is still underexplored.
  4. New Trends for Modern Machine Translation with Large Reasoning Models (2026 · 16 citations)
    This position paper from Alibaba Group and the University of Edinburgh argues that Large Reasoning Models (LRMs), which use Chain-of-Thought (CoT) reasoning, change machine translation (MT).

All works

9 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions · original ↗ · no summary yet · arXiv:2304.14402Minghao Wu et al.2023 · 105 citations
  2. Bactrian-X: Multilingual Replicable Instruction-Following Models with Low-Rank AdaptationHaonan Li, Fajri Koto, Minghao Wu, et al.arXiv:2305.15011 · 95 citations
  3. Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text IntegrationChenyang Lyu, Minghao Wu, Longyue Wang, et al.arXiv:2306.09093 · 74 citations
  4. (Perhaps) Beyond Human Translation: Harnessing Multi-Agent Collaboration for Translating Ultra-Long Literary Texts · original ↗ · no summary yet · Transactions of the Association for Computational LinguisticsMinghao Wu et al.2025 · 71 citations
  5. Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models · original ↗ · no summary yet · arXiv:2503.01461Huifeng Yin et al.2025 · 8 citations
  6. TransBench: Benchmarking Machine Translation for Industrial-Scale Applications · original ↗ · no summary yet · arXiv:2505.14244Haijun Li et al.2025 · 5 citations
  7. Rethinking Multilingual Vision-Language Translation: Dataset, Evaluation, and Adaptation · original ↗ · no summary yet · arXiv:2506.11820Xintong Wang et al.2025 · 1 citation

 Co-authors here

10 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 354 times (of the 141,840 papers whose bibliographies we read); 37 citing papers with a page here →.

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