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Shannon Zejiang Shen

Shannon Zejiang Shen has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining; often with Aakanksha Naik, Akari Asai, Alan Li.

4 papers in our library · 2025–2026

OpenAlex · h-index 3 · 7 works · 19 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 197 times in our library · ⭐ Paper Stars ranking: #7218

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

🎓Massachusetts Institute of TechnologyCambridge · United States2024–2025 · 3 paperscurrent

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).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksInformation Systems · 2 works

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

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DR Tulu: Reinforcement Learning with Evolving Rubrics for Deep Research (2025 · 28 citations)
    Open deep research (DR) agents are usually trained with reinforcement learning on short questions whose answers can be checked automatically. That kind of reward does not transfer to open-ended, long-form reports, where quality is loosely defined and checking claims requires current external evidence.
  2. SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature (2025 · 27 citations)
    Researchers increasingly want LLMs that can work directly with scientific papers. That means following detailed instructions over long article text and returning structured, evidence-backed answers. Few training resources target this skill across multiple fields. The authors address the gap with SciRIFF, a…
  3. When One LLM Drools, Multi-LLM Collaboration Rules (2026 · 14 citations)
    This position paper by Feng et al. (ACL 2026) argues that a single general-purpose LLM cannot reliably handle complex, context-dependent, or subjective settings. It contributes no new dataset or model. Instead, it makes a conceptual case that any one model leaves gaps on three fronts. It misses parts of real-world…
  4. Scaling Collaborative Effort with Agents (2026 · 7 citations)
    This position paper argues that LLM agents are mostly built and judged as one-shot task finishers. That suits poorly the many real tasks where user goals are vague and change as work proceeds. The authors present five case studies (data analysis, travel planning, financial advising, education, math discovery). In…

 Co-authors here

12 of 30 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 197 times (of the 149,439 papers whose bibliographies we read); 43 citing papers with a page here →.

Updated

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