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.
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
Affiliations
Fields, topics and concepts
📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksSocial Sciences · 4 worksGeneral Social Sciences · 2 worksSafety Research · 1 workPolitical Science and International Relations · 1 workEducation · 1 work
🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 work
Works by year
2020s
Papers
- 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. - 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… - 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… - 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
- Pranav Narayanan Venkit ×2
- Afra Feyza Akyürek
- Anisha Gunjal
- Chen Bo Calvin Zhang
- Henry Peng Zou
- Jiangshu Du
- Mukund Srinath
- Nan Zhang
- Rebecca J. Passonneau
- Renze Lou
- Shomir Wilson
- Wenting Zhao
Venues
- Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026 ×2
- Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2024
- Workshop on Gender Bias in Natural Language Processing: Workshop on Gender Bias in Natural Language Processing 2023
Cited by
Updated
This page in Markdown → https://md.research.elamur.ai/free/en/authors/vipul-gupta/