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Juntong Song

Juntong Song has 2 papers in our library, in 2024–2026, most of them on Language models & pretraining, Evaluation & benchmarks, Information retrieval & search; often with Cheng Niu, Juno Zhu, Tong Zhang.

2 papers in our library · 2024–2026

OpenAlex · h-index 5 · 8 works · 157 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 105 times in our library · ⭐ Paper Stars ranking: #5617

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 Illinois Urbana-ChampaignUrbana · United States2023–2024 · 3 papers

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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 workEvaluation & benchmarks · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 1 work

 Works by year

2020s

20261 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024 · 207 citations)
    Retrieval-augmented generation (RAG) is meant to keep large language models anchored to supplied sources, yet models still produce claims that the retrieved material does not support or that contradict it.
  2. OpenGenAlign: A Preference Dataset and Benchmark for Trustworthy Reward Modeling in Open-Ended, Long-Context Generation (2026 · 2 citations)
    Reward models are central to RLHF, but there has been no dedicated training data or benchmark for open-ended, long-context generation.

 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 105 times (of the 149,439 papers whose bibliographies we read); 26 citing papers with a page here →.

Updated

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