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Khyati Mahajan

Khyati Mahajan has 2 papers in our library, in 2022–2026, most of them on Speech act theory, Language models & pretraining, Question answering; often with Rishabh Maheshwary, Spandana Gella, Vikas Yadav.

2 papers in our library · 2022–2026

OpenAlex · ORCID · h-index 6 · 25 works · 274 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 12 citations in our library · 1 more in our library without a page yet · ⭐ Paper Star rank #6206

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

🎓University of North Carolina at CharlotteCharlotte · United States2019–2023 · 10 papers🏢ServiceNowSanta Clara · United States2025 · 3 papers🎓University of Illinois ChicagoChicago · United States2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Concepts, shelves and topics

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

📚 Shelves 5 Artificial Intelligence · 2 works Arts and Humanities · 2 works Computer Science · 2 works Language and Linguistics · 2 works Computer Vision and Pattern Recognition · 1 work

🧭 Topics 5 Dialogue systems · 1 work Speech act theory · 1 work Language models & pretraining · 1 work Question answering · 1 work Reasoning · 1 work

 Works by year

2020s

20261 paper20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Improving Dialogue Act Recognition with Augmented Data (2022 · 2 citations)
    The authors build dialogue act recognition (DAR) models for an industrial virtual assistant. They find that a BERT-based classifier fine-tuned on the Schema-Guided Dialogue (SGD) corpus scores very well on SGD but fails on in-house conversations. AFFIRM falls to 0.39 F1 and NEGATE to 0.16. The cause is low variety in…
  2. Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QA (2026)
    Iterative retrieval-augmented generation helps with multi-hop questions, but each retrieval round adds more documents to the prompt.

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. Socratic Questioning of Novice Debuggers: A Benchmark Dataset and Preliminary Evaluations (original ↗)Erfan Al-Hossami et al.2023 · Proceedings of the 18th Workshop on Innovative… · 34 citations · no summary yet
  2. Improving Dialogue Act Recognition with Augmented DataKhyati Mahajan, Soham Parikh, Quaizar Vohra, et al.Workshop on Generation, Evaluation and Metrics… · 2 citations
  3. Augmenting LLM Reasoning with Dynamic Notes Writing for Complex MultiHop QARishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, et al.Language Resources and Evaluation Conference…

 Co-authors here

3 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 12 times (of the 141,840 papers whose bibliographies we read).

Updated

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