Elamur Research⌕
☰
home / authors / Ali Payani

Ali Payani

Ali Payani has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Low-resource languages, Reasoning; often with Amir Saeidi, Chitta Baral, Fan Yang.

4 papers in our library · 2024–2026

OpenAlex · ORCID · Wikidata · h-index 10 · 79 works · 340 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 93 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6437

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

🏢Cisco SystemsSan Jose · United States2021–2026 · 23 paperscurrent🎓Arizona State UniversityTempe · United States2026 · 4 paperscurrent🤝Cisco And Cisco ProductionsRiver Forest · United States2026 · 1 papercurrent🎓Cisco CollegeCisco · United States2026 · 1 papercurrent🎓Georgia Institute of TechnologyAtlanta · United States2016–2021 · 12 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 · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 4 worksLow-resource languages · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper20251 paper20242 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages (2024 · 95 citations)
    LLMs are trained on corpora dominated by English. For example, about 92.65% of GPT-3 tokens and 89.7% of LlaMa2 pre-training data are English. As a result, low-resource languages get weaker support, yet there are few quantitative ways to measure this gap. The authors propose Language Ranker, an intrinsic metric that…
  2. When is Tree Search Useful for LLM Planning? It Depends on the Discriminator (2024 · 28 citations)
    The paper asks when sophisticated planning strategies actually help LLM agents on multi-step tasks. The authors model an agent as three components: a generator that proposes programs, a discriminator that scores them as a yes/no correctness question, and a planning method. They compare re-ranking with iterative…
  3. Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World Model (2025 · 7 citations)
    Chain-of-Thought prompting lets language models reason step by step. However, purely textual chains are hard to check and often contain inconsistent or hallucinated steps. Fully formal alternatives, such as first-order logic or programs, generalize poorly across task types. Xiong et al. propose SWAP, a semi-formal…
  4. FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments (2026)
    LLM-driven agents in multi-turn customer-service tool-use settings often fail because early wrong decisions compound over the conversation.

All works

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

  1. Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource LanguagesProceedings of the AAAI Conference on Artificial IntelligenceZihao Li, Yucheng Shi, Zirui Liu, et al.2024 · 95 citations
  2. When is Tree Search Useful for LLM Planning? It Depends on the DiscriminatorAnnual Meeting of the Association for Computational Linguistics (ACL)Ziru Chen, Michael White, Ray Mooney, et al.2024 · 28 citations
  3. Investigating the Shortcomings of LLMs in Step-by-Step Legal Reasoning ↗Findings of the Association for Computational Linguistics: NAACL 2025 · no summary yetVenkatesh Mishra et al.2025 · 13 citations
  4. Deliberate Reasoning in Language Models as Structure-Aware Planning with an Accurate World ModelAnnual Meeting of the Association for Computational Linguistics (ACL)Siheng Xiong, Ali Payani, Yuan Yang, et al.2025 · 7 citations
  5. FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use EnvironmentsFindings of the Association for Computational LinguisticsAmir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay, et al.2026

 Co-authors here

12 of 16 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 93 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

Updated

↑ back to top

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