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Haidar Khan

Haidar Khan has 3 papers in our library, in 2022–2024, most of them on Language models & pretraining, Evaluation & benchmarks, Multilingual & cross-lingual NLP; often with M Saiful Bari, Andy Rosenbaum, Anna Rumshisky.

3 papers in our library · 2022–2024

OpenAlex · ORCID · h-index 11 · 33 works · 718 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 270 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5856

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

🏢MetaMenlo Park · United States2026 · 1 papercurrent🏢AmazonSeattle · United States2019–2023 · 11 papers🎓Rensselaer Polytechnic InstituteTroy · United States2017–2025 · 6 papers🎓University of Massachusetts AmherstAmherst Center · United States2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 3 worksEvaluation & benchmarks · 2 worksMultilingual & cross-lingual NLP · 1 work

 Works by year

2020s

20242 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024 · 105 citations)
    LLM evaluation results often conflict across studies because each study sets up its experiments differently.
  2. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model (2022 · 84 citations)
    Large language models that learn from a few examples in the prompt have mostly been decoder-only. Their left-to-right attention can be a handicap when the input is long. This Amazon Alexa AI paper asks whether an encoder-decoder model can be an equally strong in-context learner.
  3. When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards (2024 · 74 citations)
    The authors, from NCAI (SDAIA, Riyadh), test whether LLM leaderboards built on multiple-choice question (MCQ) benchmarks can be trusted.

All works

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

  1. A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and RecommendationsConference on Empirical Methods in Natural Language Processing (EMNLP)Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, et al.2024 · 105 citations
  2. AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq ModelarXiv:2208.01448Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald, et al.2022 · 84 citations
  3. When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model LeaderboardsarXiv:2402.01781Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay, et al.2024 · 74 citations
  4. Using multiple ASR hypotheses to boost i18n NLU performance ↗Proceedings of the 17th International Conference on Natural Language Processing (ICON) · no summary yetCharith Peris et al.2020 · 1 citation

 Co-authors here

12 of 23 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 270 times (of the 149,439 papers whose bibliographies we read); 136 citing papers with a page here →.

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