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Letian Peng

Letian Peng has 2 papers in our library, in 2026, most of them on Language models & pretraining, Evaluation & benchmarks; often with Jingbo Shang, Gaowen Liu, Jayanth Srinivasa.

2 papers in our library · 2026

OpenAlex · ORCID · h-index 3 · 41 works · 37 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 4 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #10720

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

🎓University of California San DiegoSan Diego · United States2023–2026 · 25 paperscurrent🎓University of Notre DameNotre Dame · United States2026 · 1 papercurrent🎓University of San DiegoSan Diego · United States2026 · 1 papercurrent🎓Shanghai Jiao Tong UniversityShanghai · China2021–2024 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 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).Computer Science · 2 worksArts and Humanities · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026 · 4 citations)
    The paper asks how well language models can absorb new facts after pre-training. Earlier knowledge-injection studies mostly relied on synthetic data drawn from noisy Wikipedia snapshots, and their questions were often poorly specified.
  2. Deriving Character Logic from Storyline as Codified Decision Trees (2026)
    Role-playing (RP) agents built on LLMs usually rely on free-text character profiles. These profiles are hard to verify, cannot be executed, and tend to mix behaviors from different situations, so agents act inconsistently. The authors propose Codified Decision Trees (CDT), which learn a character's behavioral logic…

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge InjectionAnnual Meeting of the Association for Computational Linguistics (ACL)Yuwei Zhang, Wenhao Yu, Shangbin Feng, et al.2026 · 4 citations
  2. Deriving Character Logic from Storyline as Codified Decision TreesAnnual Meeting of the Association for Computational Linguistics (ACL)Letian Peng, Kun Zhou, Longfei Yun, et al.2026
  3. C-World: A Computer Use Agent Environment Creator ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZiqiao Xi et al.2026

 Co-authors here

9 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 4 times (of the 149,439 papers whose bibliographies we read).

Updated

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