Elamur Research⌕
☰
home / authors / Abulhair Saparov

Abulhair Saparov

Abulhair Saparov has 2 papers in our library, in 2022–2023, most of them on Language models & pretraining, Reasoning; often with He He, Najoung Kim, Nitish Joshi.

2 papers in our library · 2022–2023

OpenAlex · ORCID · Wikidata · h-index 9 · 49 works · 910 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 370 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #4089

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

🎓Purdue University West LafayetteWest Lafayette · United States2024–2026 · 9 paperscurrent🎓New York UniversityNew York · United States2022–2024 · 10 papers🎓Carnegie Mellon UniversityPittsburgh · United States2015–2025 · 8 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.

 Concepts, shelves and topics

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 works

 Works by year

2020s

20231 paperAbulhair Saparov's papers of 202320221 paperAbulhair Saparov's papers of 2022

 Papers

The most cited first (OpenAlex and our library).

  1. Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought (2022 · 239 citations)
    High accuracy on reasoning benchmarks does not show how large language models reach their answers. They may recall facts from pretraining or exploit shortcuts instead of reasoning. Saparov and He address this by checking the chain-of-thought (CoT) itself, step by step, rather than only the final label. They build…
  2. Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples (2023 · 82 citations)
    Chain-of-thought prompting lets large language models do some deductive reasoning. However, earlier tests mostly used modus ponens and drew test items from the same distribution as the few-shot demonstrations. This study asks whether LLMs reason deductively in a general sense, meaning whether they can produce proofs…

All works

5 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. Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-ThoughtarXiv:2210.01240Abulhair Saparov, He He2022 · 239 citations
  2. Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD ExamplesConference on Neural Information Processing Systems (NeurIPS)Abulhair Saparov, Richard Yuanzhe Pang, Vishakh Padmakumar, et al.2023 · 82 citations
  3. Foundational Challenges in Assuring Alignment and Safety of Large Language Models ↗arXiv:2404.09932 · no summary yetUsman Anwar et al.2024 · 77 citations
  4. A Practical Review of Mechanistic Interpretability for Transformer-Based Language Models ↗arXiv:2407.02646 · no summary yetDaking Rai et al.2024 · 57 citations
  5. Retrieval-Augmented Chain-of-Thought in Semi-structured Domains ↗Proceedings of the Natural Legal Language Processing Workshop 2023 · no summary yetVaibhav Mavi, Abulhair Saparov, Chen Zhao2023 · 9 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 370 times (of the 141,840 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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