1 citations · 2 across the 6 of their papers we have counts for
6 papers
BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences?
Soumadeep Saha, Krish Sharma, Akshay Chaturvedi +1
Although reinforcement learning with verifiable rewards (RLVR) has improved the performance of large language models (LLMs) across a variety of reasoning tasks, there is significan…
TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination
Omar Naim, Krish Sharma, Niyar R Barman +1
Large Language Models (LLMs) typically come with a fixed architecture, despite growing evidence that not all layers contribute equally to every downstream task. We introduce TALE (…
DIMSUM: Discourse in Mathematical Reasoning as a Supervision Module
Krish Sharma, Niyar R Barman, Akshay Chaturvedi +1
We look at reasoning on GSM8k, a dataset of short texts presenting primary school, math problems. We find, with Mirzadeh et al. (2024), that current LLM progress on the data set ma…
The Brittleness of AI-Generated Image Watermarking Techniques: Examining Their Robustness Against Visual Paraphrasing Attacks
Niyar R Barman, Krish Sharma, Ashhar Aziz +7
The rapid advancement of text-to-image generation systems, exemplified by models like Stable Diffusion, Midjourney, Imagen, and DALL-E, has heightened concerns about their potentia…
Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index
Megha Chakraborty, S. M Towhidul Islam Tonmoy, S M Mehedi Zaman +9
With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-gener…
Serverless Federated Learning with flwr-serverless
Sanjeev V. Namjoshi, Reese Green, Krishi Sharma +1
Federated learning is becoming increasingly relevant and popular as we witness a surge in data collection and storage of personally identifiable information. Alongside these develo…