Elamur Research⌕
☰
home / authors / Guojie Song

Guojie Song

Guojie Song has 3 papers in our library, in 2024–2026, most of them on Reasoning, Language models & pretraining; often with Haonan Dong, Haoran Ye, Kehan Jiang.

3 papers in our library · 2024–2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 58 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #7306

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

🎓Peking UniversityBeijing · China2026 · 4 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 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksPsychology · 1 workSafety Research · 1 workSocial Psychology · 1 workSocial Sciences · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Reasoning · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language Models (2024 · 43 citations)
    LLMs increasingly act as stand-ins for people in education, healthcare and other public-facing settings, so it matters which values they express and how well they grasp human values.
  2. FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models (2026 · 15 citations)
    Large Reasoning Models such as DeepSeek-R1 often produce several alternative solutions inside a single chain of thought.
  3. NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons (2026 · 13 citations)
    Large Reasoning Models still make three kinds of mistakes. Within a step, they get calculations or derivations wrong. Across steps, they loop or stall. At the instance level, they over-think easy questions. Earlier fixes target only one of these, rely on opaque RL training, and offer little insight or control.

All works

4 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. ValueBench: Towards Comprehensively Evaluating Value Orientations and Understanding of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yuanyi Ren, Haoran Ye, Hanjun Fang, et al.2024 · 43 citations
  2. FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Kehan Jiang, Haonan Dong, Zhaolu Kang, et al.2026 · 15 citations
  3. NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-NeuronsAnnual Meeting of the Association for Computational Linguistics (ACL)Haonan Dong, Kehan Jiang, Haoran Ye, et al.2026 · 13 citations
  4. Context-Value-Action Architecture for Value-Driven Large Language Model Agents ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetTianZe Zhang et al.2026

 Co-authors here

7 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 58 times (of the 149,439 papers whose bibliographies we read); 16 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/guojie-song/