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Howard Yen

Howard Yen has 3 papers in our library, in 2023–2025, most of them on Language models & pretraining, Evaluation & benchmarks, Reasoning; often with Danqi Chen, Tianyu Gao, Alexander Wettig.

3 papers in our library · 2023–2025

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📄 3 works in our library · 🔗 Cited 417 times in our library · ⭐ Paper Stars ranking: #2966

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

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksEvaluation & benchmarks · 1 workInformation retrieval & search · 1 workReasoning · 1 work

 Works by year

2020s

20251 paperHow to Train Long-Context Language Models (Effectively)20241 paperBRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval20231 paperEnabling Large Language Models to Generate Text with Citations

 Papers

The most cited first (OpenAlex and our library).

  1. Enabling Large Language Models to Generate Text with Citations (2023 · 477 citations)
    LLMs used for information seeking often hallucinate, and their answers are hard to check without sources. Earlier work on making models cite evidence relied on commercial search engines, closed models and costly human judging, so its results were hard to reproduce or compare. The authors introduce ALCE, a…
  2. How to Train Long-Context Language Models (Effectively) (2025 · 84 citations)
    The paper asks how to turn a short-context pre-trained LM into a strong long-context model through continued pre-training followed by SFT.
  3. BRIGHT: A Realistic and Challenging Benchmark for Reasoning-Intensive Retrieval (2024 · 70 citations)
    Standard retrieval benchmarks such as BEIR and MTEB mostly test search-engine-style questions. In these, relevant documents share many words or meanings with the query. Many real needs are harder. A programmer may need documentation for a function that is never named in the question, or a student may need a problem…

 Co-authors here

8 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 417 times (of the 141,840 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

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