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Bo Zeng

Bo Zeng has 4 papers in our library, in 2024–2026, most of them on Reasoning, Machine translation, Multilingual & cross-lingual NLP; often with Chenyang Lyu, Hao Wang, Tianqi Shi.

4 papers in our library · 2024–2026

OpenAlex · h-index 0 · 3 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 89 times in our library · ⭐ Paper Stars ranking: #8022

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

🏢Alibaba GroupHangzhou · China2026 · 3 paperscurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 4Research subjects and languages the work is about.Reasoning · 2 worksMultilingual & cross-lingual NLP · 1 workEfficient models & compression · 1 workMachine translation · 1 work

 Works by year

2020s

20261 paper20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Marco-o1: Towards Open Reasoning Models for Open-Ended Solutions (2024 · 82 citations)
    Marco-o1, from Alibaba's MarcoPolo Team, is an early open attempt to reproduce the behavior of OpenAI's o1-style large reasoning models (LRM).
  2. Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models (2025 · 8 citations)
    Small models are often taught to reason by fine-tuning them on long chain-of-thought (CoT) traces from large reasoning models such as DeepSeek-R1.
  3. Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025 · 5 citations)
    Most benchmarks for LLM instruction following are English-only or plain machine translations, so they handle constraints that don't transfer across languages poorly.
  4. M²PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026)
    The paper tackles unreliable reward signals in preference-based alignment of LLMs for machine translation.

 Co-authors here

12 of 23 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 89 times (of the 149,439 papers whose bibliographies we read); 35 citing papers with a page here →.

Updated

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