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Vipul Gupta

Vipul Gupta has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning; often with Pranav Narayanan Venkit, Afra Feyza Akyürek, Anisha Gunjal.

4 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 23 · 118 works · 1,591 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 82 times in our library · ⭐ Paper Stars ranking: #6580

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024 · 71 citations)
    This EMNLP 2024 paper asks whether large language models can take on part of the growing peer-review burden in NLP, both as reviewers and as meta-reviewers who audit individual reviews.
  2. Sociodemographic Bias in Language Models: A Survey and Forward Path (2023 · 40 citations)
    Language models tend to absorb social prejudices from their training corpora. When they behave unequally across demographic groups, they can cause real harm once deployed. Gupta et al. review roughly ten years of research on this sociodemographic bias. They ran keyword searches in the ACL Anthology, NeurIPS, FAccT…
  3. PRBench: Large-Scale Expert Rubrics for Evaluating High-Stakes Professional Reasoning (2026 · 7 citations)
    Most LLM benchmarks reward short, checkable answers in math, science and coding. They reveal little about open-ended, high-stakes professional work. Preference arenas, the usual option for open-ended evaluation, produce noisy scores without expert grounding. The Scale AI authors address this gap with PRBench, a set…
  4. Development and Benchmarking of a Blended Human-AI Qualitative Research Assistant (2026)
    Manual qualitative coding is slow and hard to scale. Human coders tire and drift in how they interpret codes, and earlier computational tools could not match human nuance. The authors, from RAND Corporation, present Muse, an LLM-based assistant built with feedback from over 100 qualitative researchers. It is in…

 Co-authors here

12 of 14 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 82 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

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