2 citations · 3 across the 8 of their papers we have counts for
9 papers
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…
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…
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…
RONA: Pragmatically Diverse Image Captioning with Coherence Relations
Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee
Writing Assistants (e.g., Grammarly, Microsoft Copilot) traditionally generate diverse image captions by employing syntactic and semantic variations to describe image components. H…
CORDIAL: Can Multimodal Large Language Models Effectively Understand Coherence Relationships?
Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee
Multimodal Large Language Models (MLLMs) are renowned for their superior instruction-following and reasoning capabilities across diverse problem domains. However, existing benchmar…
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…
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…
ANNA: Abstractive Text-to-Image Synthesis with Filtered News Captions
Aashish Anantha Ramakrishnan, Sharon X. Huang, Dongwon Lee
Advancements in Text-to-Image synthesis over recent years have focused more on improving the quality of generated samples using datasets with descriptive prompts. However, real-wor…