8 papers
ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis
Aashish Anantha Ramakrishnan, Sharon X. Huang, Dongwon Lee
Text-to-image (T2I) models have achieved remarkable progress in high-quality image synthesis, yet most benchmarks rely on simple, self-contained prompts, failing to capture the com…
Generative Active Testing: Efficient LLM Evaluation via Proxy Task Adaptation
Aashish Anantha Ramakrishnan, Ardavan Saeedi, Hamid Reza Hassanzadeh +2
With the widespread adoption of pre-trained Large Language Models (LLM), there exists a high demand for task-specific test sets to benchmark their performance in domains such as he…
Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation
Dominik Macko, Aashish Anantha Ramakrishnan, Jason Samuel Lucas +4
Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While huma…
LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles
Ho Yin 'Sam' Ng, Ting-Yao Hsu, Aashish Anantha Ramakrishnan +8
Figure captions are crucial for helping readers understand and remember a figure's key message. Many models have been developed to generate these captions, helping authors compose…
IRONIC: Coherence-Aware Reasoning Chains for Multi-Modal Sarcasm Detection
Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee
Interpreting figurative language such as sarcasm across multi-modal inputs presents unique challenges, often requiring task-specific fine-tuning and extensive reasoning steps. Howe…
From Intentions to Techniques: A Comprehensive Taxonomy and Challenges in Text Watermarking for Large Language Models
Harsh Nishant Lalai, Aashish Anantha Ramakrishnan, Raj Sanjay Shah +1
With the rapid growth of Large Language Models (LLMs), safeguarding textual content against unauthorized use is crucial. Watermarking offers a vital solution, protecting both - LLM…