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Alireza Salemi

Alireza Salemi has 4 papers in our library, in 2024–2025, most of them on Evaluation & benchmarks, Language models & pretraining, Question answering; often with Hamed Zamani, Fuli Feng, Jinghao Zhang.

4 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 8 · 38 works · 466 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 186 times in our library · ⭐ Paper Stars ranking: #3997

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 Massachusetts AmherstAmherst Center · United States2023–2026 · 30 paperscurrent🎓University of TehranTehran · Iran2021–2023 · 3 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksInformation Systems · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 1 workEvaluation & benchmarks · 2 worksInformation retrieval & search · 1 workQuestion answering · 1 work

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

 Works by year

2020s

20252 papers20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. LaMP: When Large Language Models Meet Personalization (2024 · 246 citations)
    Most NLP benchmarks evaluate models the same way for every user, so there has been little infrastructure for studying how LLMs can tailor outputs to individuals.
  2. Evaluating Retrieval Quality in Retrieval-Augmented Generation (2024 · 181 citations)
    Retrieval components in RAG systems are hard to assess. Full end-to-end evaluation costs a lot of compute and gives only one score per result list. Classic relevance judgments, such as KILT provenance labels, turn out to track downstream RAG quality poorly. Salemi and Zamani propose eRAG, which uses the RAG system's…
  3. Personalized Generation In Large Model Era: A Survey (2025 · 24 citations)
    Work on generating content tailored to individual users has grown separately in NLP, computer vision and information retrieval.
  4. LaMP-QA: A Benchmark for Personalized Long-form Question Answering (2025 · 12 citations)
    Research on personalization in information seeking has mostly focused on retrieval. Earlier generation benchmarks such as LaMP and LongLaMP cover content creation (e.g., emails), not answering a user's own questions. LaMP-QA fills this gap with a benchmark for personalized long-form answer 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 186 times (of the 149,439 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated

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