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Mihir Parmar

Mihir Parmar has 6 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Chitta Baral, Neeraj Varshney, Man Luo.

6 papers in our library · 2023–2026

OpenAlex · h-index 15 · 57 works · 986 citations OpenAlex, 2026-10-14

📄 6 works in our library · 🔗 Cited 757 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5629

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

🎓Arizona State UniversityTempe · United States2022–2026 · 25 paperscurrent🏢GoogleMountain View · United States2026 · 3 paperscurrent🏥Mayo Clinic HospitalPhoenix · United States2026 · 1 papercurrent🏥Mayo Clinic in ArizonaScottsdale · United States2026 · 1 papercurrent🎓University of KentuckyLexington · United States2026 · 1 papercurrent🎓King Saud UniversityRiyadh · Saudi Arabia2013–2017 · 8 papers🎓Jawaharlal Nehru Technological University, HyderabadHyderabad · India2018–2019 · 6 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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 6 worksArtificial Intelligence · 6 worksArts and Humanities · 5 worksLanguage and Linguistics · 5 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 5 worksReasoning · 4 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20261 paper20251 paper20243 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Models (2024 · 93 citations)
    Most logical reasoning benchmarks for LLMs either blend logic with other skills (LogiQA, ReClor) or stick to one logic type and a few rules such as modus ponens (FOLIO, ProntoQA).
  2. Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language Models (2024 · 39 citations)
    Benchmarks for logical reasoning in LLMs usually test only single inference steps, rely on a few rules such as modus ponens, or cover only one kind of logic.
  3. A Survey of Small Language Models (2024 · 31 citations)
    Large language models perform well but need heavy compute, memory and data, so they usually run on centralized, specialized hardware.
  4. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models (2023 · 27 citations)
    The paper asks how well language models handle logical reasoning expressed in natural language. Formal knowledge-representation systems needed heavy expert effort and did not scale. The authors first survey the field. They cover three reasoning types (deductive, inductive, abductive), the usual task formats, how…
  5. PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem Solving (2025 · 16 citations)
    LLM-based agents and test-time search methods often fail on "natural planning" tasks, such as scheduling meetings or planning trips described in plain language.
  6. CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026 · 1 citation)
    Preference-based alignment of LLMs depends on human-labelled response pairs, which are costly to collect. Self-rewarding pipelines produce such pairs cheaply, but they can amplify the model's own mistakes. Active preference learning keeps labels trustworthy, yet it leaves most of the unlabeled pool unused. CoAct…

All works

7 works of theirs in our library, the most cited first; 6 with a page here, the others link their original until we summarise them.

  1. Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks ↗Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing · no summary yetYizhong Wang et al.2022 · 849 citations
  2. LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mihir Parmar, Nisarg Patel, Neeraj Varshney, et al.2024 · 93 citations
  3. Multi-LogiEval: Towards Evaluating Multi-Step Logical Reasoning Ability of Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Nisarg Patel, Mohith Kulkarni, Mihir Parmar, et al.2024 · 39 citations
  4. A Survey of Small Language ModelsarXiv:2410.20011Chien Van Nguyen, Xuan Shen, Ryan Aponte, et al.2024 · 31 citations
  5. Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language ModelsarXiv:2310.00836Man Luo, Shrinidhi Kumbhar, Ming shen, et al.2023 · 27 citations
  6. PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem SolvingConference on Empirical Methods in Natural Language Processing (EMNLP)Mihir Parmar, Xin Liu, Palash Goyal, et al.2025 · 16 citations
  7. CoAct: Co-Active LLM Preference Learning with Human-AI SynergyAnnual Meeting of the Association for Computational Linguistics (ACL)Ruiyao Xu, Mihir Parmar, Tiankai Yang, et al.2026 · 1 citation

 Co-authors here

12 of 27 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 757 times (of the 149,439 papers whose bibliographies we read); 166 citing papers with a page here →.

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