--- title: "Arash Asgari — author profile and papers" description: "Arash Asgari, University of Toronto: 2 papers on Elamur Research (2026), h-index 2 — affiliations, topics, co-authors and who cites them in our library." canonical: "https://research.elamur.ai/free/en/authors/arash-asgari/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/arash-asgari/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Arash Asgari # Arash Asgari 2 papers on this site · 2026 current affiliation [University of Toronto](https://md.research.elamur.ai/free/en/universities/university-of-toronto/), Toronto, Canada; [York University](https://md.research.elamur.ai/free/en/universities/york-university/), Toronto, Canada; [York University](https://md.research.elamur.ai/free/en/universities/york-university-us/), York, United States; [Vector Institute](https://md.research.elamur.ai/free/en/labs/vector-institute/), Toronto, Canada Arash Asgari has 2 papers on this site, in 2026, most of them on [Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/), [Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/); often with [Laleh Seyyed-Kalantari](https://md.research.elamur.ai/free/en/authors/laleh-seyyed-kalantari/), [Ali Emami](https://md.research.elamur.ai/free/en/authors/ali-emami/), [Hassan Sajjad](https://md.research.elamur.ai/free/en/authors/hassan-sajjad/). [OpenAlex](https://openalex.org/A5094313083) · [ORCID](https://orcid.org/0000-0002-1424-5290) · [**h-index 2**](https://openalex.org/authors/A5094313083) · [12 works](https://openalex.org/works?filter=author.id:A5094313083) · [8 citations](https://openalex.org/works?filter=author.id:A5094313083&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [2 works on this site](https://md.research.elamur.ai/free/en/authors/arash-asgari/#papers) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Arash+Asgari) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Arash+Asgari) · [dblp](https://dblp.org/search?q=Arash+Asgari) · [ACL Anthology](https://aclanthology.org/search/?q=Arash+Asgari) ## Affiliations - [York University](https://md.research.elamur.ai/free/en/universities/york-university/) · Toronto, Canada —current · 2025–2026 on 3 papers - [Vector Institute](https://md.research.elamur.ai/free/en/labs/vector-institute/) · Toronto, Canada —current · 2025–2026 on 2 papers - [University of Toronto](https://md.research.elamur.ai/free/en/universities/university-of-toronto/) · Toronto, Canada —current · 2026 on 1 paper - [York University](https://md.research.elamur.ai/free/en/universities/york-university-us/) · York, United States —current · 2026 on 1 paper - [Sharif University of Technology](https://ror.org/024c2fq17) · Tehran, Iran —2023–2024 on 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. ## Topics [Language models & pretraining 1](https://md.research.elamur.ai/free/en/topics/language-models/) · [Evaluation & benchmarks 1](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) ## Works by year ### 2020s [**2026** 2 papers](https://md.research.elamur.ai/free/en/authors/arash-asgari/year/2026/) ## Papers The most cited first (OpenAlex and our library). 1. **[Quantifying Metric and Model Agreement in Bias Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/asgari-2026-quantifying-metric-model-agreement-bias/)** *(2026)* The bias of large language models is evaluated with dozens of metrics and benchmarks. However, until now there has been no way to check whether these tools agree with one another and whether they rank models consistently. The authors propose two measures. The Metric Agreement Score (MeAS) shows how similarly… 2. **[ZIP: Quantifying Which Words Matter in Zero-Shot Instructional Prompts](https://md.research.elamur.ai/free/en/papers/sadr-2026-zip-quantifying-which-words-matter/)** *(2026)* Zero-shot instructional prompts such as the Chain-of-Thought cue "Let's think step-by-step" reliably improve LLM accuracy, but it has been unclear which individual words produce the gain. ## Co-authors here 3 co-authors with a page, the most papers together first. - [Laleh Seyyed-Kalantari](https://md.research.elamur.ai/free/en/authors/laleh-seyyed-kalantari/) ×2 - [Ali Emami](https://md.research.elamur.ai/free/en/authors/ali-emami/) - [Hassan Sajjad](https://md.research.elamur.ai/free/en/authors/hassan-sajjad/)