most citedAd Headline Generation using Self-Critical Masked Language Model

14 citations · 21 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CL20267 cited

COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation

Yashal Shakti Kanungo, Gyanendra Das, Pooja A +1

Online ads are essential to all businesses and ad headlines are one of their core creative component. Existing methods can generate headlines automatically and also optimize their…

cs.CL202614 cited

Ad Headline Generation using Self-Critical Masked Language Model

Yashal Shakti Kanungo, Sumit Negi, Aruna Rajan

For any E-commerce website it is a nontrivial problem to build enduring advertisements that attract shoppers. It is hard to pass the creative quality bar of the website, especially…

cs.CL2026

Fast-dLLM++: Fréchet Profile Decoding for Faster Diffusion LLM Inference

Siva Rajesh Kasa, Yasong Dai, Sumit Negi +1

Diffusion large language models promise parallel token generation, yet inference remains bottlenecked by deciding which masked tokens can be safely committed together. Fast-dLLM ad…

cs.CL2026

DIVERSED: Relaxed Speculative Decoding via Dynamic Ensemble Verification

Ziyi Wang, Siva Rajesh Kasa, Ankith M S +6

Speculative decoding is an effective technique for accelerating large language model inference by drafting multiple tokens in parallel. In practice, its speedup is often bottleneck…

cs.CV2026

Rethinking Test Time Scaling for Flow-Matching Generative Models

Qingtao Yu, Changlin Song, Minghao Sun +6

The performance of text-to-image diffusion models may be improved at test-time by scaling computation to search for a generated image that maximizes a given reward function. While…

cs.CR2025

The Hidden Cost of Modeling P(X): Vulnerability to Membership Inference Attacks in Generative Text Classifiers

Owais Makroo, Siva Rajesh Kasa, Sumegh Roychowdhury +4

Membership Inference Attacks (MIAs) pose a critical privacy threat by enabling adversaries to determine whether a specific sample was included in a model's training dataset. Despit…