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Rahul Gupta

21 papers hereh-index 101.1k citations30 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • middle author13
  • last author6

Across the 20 of 21 papers where every author was matched, so the position is known.

fields
  • cs.CL12
  • cs.AI7
  • cs.LG2
same name
  • Rahul Gupta — 24 papers, h 10
  • Rahul Gupta — 14 papers, h 11
  • Rahul Gupta — 14 papers, h 4
  • Rahul Gupta — 13 papers, h 5
  • Rahul Gupta — 10 papers, h 20
  • Rahul Gupta — 9 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212026
most citedBOLD: Dataset and Metrics for Measuring Biases in Open-Ended Language Generation

208 citations · 269 across the 20 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CL2026

From Interpretability to Control: Insights from Six Years of the TrustNLP Workshop

Rahul Gupta, Abhinav Mohanty, Anaelia Ovalle +10

The Workshop on Trustworthy Natural Language Processing (TrustNLP), co-located with major ACL conferences since 2021, has grown from 8 proceedings papers to 41 over six editions, d…

cs.AI2026

Code Monitor Red Teaming for Public-Test-Passing Code

Junchi Liao, Jiawen Deng, Fuji Ren

Visible tests are a common gate for LLM-generated code, but passing them does not certify specification correctness. We study a deployment-like monitoring problem: after code has p…

cs.CL2026

SWAN: Semantic Watermarking with Abstract Meaning Representation

Ziping Ye, Gourab Dey, Christos Christodoulopoulos +7

We introduce SWAN (Semantic Watermarking with Abstract Meaning Representation), a novel framework that embeds watermark signatures into the semantic structure of a sentence using A…

cs.AI2026

Emergent Strategic Reasoning Risks in AI: A Taxonomy-Driven Evaluation Framework

Tharindu Kumarage, Lisa Bauer, Yao Ma +7

As reasoning capacity and deployment scope grow in tandem, large language models (LLMs) gain the capacity to engage in behaviors that serve their own objectives, a class of risks w…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.