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Parishad BehnamGhader

Parishad BehnamGhader has 2 papers in our library, in 2024, most of them on Language models & pretraining, Question answering, Evaluation & benchmarks; often with Siva Reddy, Vaibhav Adlakha, Dzmitry Bahdanau.

2 papers in our library · 2024

OpenAlex · ORCID · h-index 4 · 17 works · 109 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 305 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #3945

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

🎓McGill UniversityMontreal · Canada2022–2026 · 10 paperscurrent🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2022–2026 · 9 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.

 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 · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputer Vision and Pattern Recognition · 1 workInformation Systems · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workEvaluation & benchmarks · 1 workQuestion answering · 1 work

 Works by year

2020s

20242 papersParishad BehnamGhader's papers of 2024

 Papers

The most cited first (OpenAlex and our library).

  1. LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders (2024 · 209 citations)
    Decoder-only LLMs lead most NLP benchmarks, but they are rarely used as text embedders. One reason is causal attention, which lets each token see only its left context. The authors propose LLM2Vec, an unsupervised, label-free recipe that turns any decoder-only model into a general-purpose encoder in three stages.…
  2. Evaluating Correctness and Faithfulness of Instruction-Following Models for Question Answering (2024 · 145 citations)
    Instruction-following LLMs can answer questions when given retrieved passages and a task instruction, with no extra training.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. LLM2Vec: Large Language Models Are Secretly Powerful Text EncodersarXiv:2404.05961Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.2024 · 209 citations
  2. Evaluating Correctness and Faithfulness of Instruction-Following Models for Question AnsweringTransactions of the Association for Computational Linguistics (TACL)Vaibhav Adlakha, Parishad BehnamGhader, Xing Han Lu, et al.2024 · 145 citations
  3. DeepSeek-R1 Thoughtology: Let's think about LLM ReasoningarXiv:2504.07128 · original ↗ · no summary yetSara Vera Marjanovic et al.2025 · 44 citations

 Co-authors here

5 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 305 times (of the 141,840 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/parishad-behnamghader/