activity
20212026
most citedLearning to Scaffold: Optimizing Model Explanations for Teaching

11 citations · 30 across the 23 of their papers we have counts for

collaborators
Showing 2026Show all

5 papers · 1 filter

cs.AI2026

The Privacy-Hallucination Tradeoff in Differentially Private Language Models

Krithika Ramesh, Krishna Pillutla, Danish Pruthi +1

Both privacy and factual accuracy are paramount in high-stakes domains like healthcare. Concerningly, we uncover and investigate a privacy-hallucination tradeoff in differentially…

cs.CY2026

Expectations and Practices around AI Disclosure in CS Research

Arati Mohapatra, Danish Pruthi

As generative AI tools find increasing use in research workflows, ongoing debates on their impact, appropriateness and responsible use have led policymakers to enact policies to di…

cs.CL2026

Evaluating Reasoning Models for Queries with Presuppositions

Rose Sathyanathan, Kinshuk Vasisht, Danish Pruthi

Millions of users turn to AI models for their information needs. It is conceivable that a large number of user queries contain assumptions that may be factually inaccurate. Prior w…

cs.CL2026

Policies Permitting LLM Use for Polishing Peer Reviews Are Currently Not Enforceable

Rounak Saha, Gurusha Juneja, Dayita Chaudhuri +3

A number of scientific conferences and journals have recently enacted policies that prohibit LLM usage by peer reviewers, except for polishing, paraphrasing, and grammar correction…

cs.CV2026

Where Do Images Come From? Analyzing Captions to Geographically Profile Datasets

Abhipsa Basu, Yugam Bahl, Kirti Bhagat +3

Recent studies show that text-to-image models often fail to generate geographically representative images, raising concerns about the representativeness of their training data and…