6 papers · 1 filter
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…