7 accepted papers
Recent efforts to integrate low-rank adaptation (LoRA) with the Mixture-of-Experts (MoE) have managed to achieve performance comparable to full-parameter fine-tuning by tuning much fewer parameters. Despite promising results, research on improving the efficiency and expert analysis of LoRA with MoE…
Large language models are increasingly becoming a cornerstone technology in artificial intelligence, the sciences, and society as a whole, yet the optimal strategies for dataset composition and filtering remain largely elusive. Many of the top-performing models lack transparency in their dataset cur…
Visual language tasks such as Visual Question Answering (VQA) or Visual Entailment (VE) require AI models to comprehend and reason with both visual and textual content. Driven by the power of Large Language Models (LLMs), two prominent methods have emerged: (1) the hybrid integration between LLMs an…
Sketch recognition has been studied for decades, but it is far from solved. Drawing styles are highly variable across people and adapting to idiosyncratic visual expressions requires data-efficient learning. Explainability also matters, so that users can see why a system got confused about something…
Emotion and empathy are examples of human qualities lacking in many human-machine interactions. The goal of our work is to generate engaging dialogue grounded in a user-shared image with increased emotion and empathy while minimizing socially inappropriate or offensive outputs. We release the Neural…
Visual Relation Detection is currently one of the most popular problems for visual understanding. Many deep-learning models are designed for relation detection on images and have achieved impressive results. However, deep-learning models have several serious problems, including poor training-efficie…
Generating formal-language programs represented by relational tuples, such as Lisp programs or mathematical operations, to solve problems stated in natural language is a challenging task because it requires explicitly capturing discrete symbolic structural information implicit in the input. However,…