Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Public sector institutions that consult citizens to inform decision-making face the challenge of
evaluating the contributions made by citizens. This evaluation has important democratic …

State-of-the-art supervised NLP models achieve high accuracy but are also susceptible to
failures on inputs from low-data regimes, such as domains that are not represented in …

Instruction tuning (IT) achieves impressive zero-shot generalization results by training large
language models (LLMs) on a massive amount of diverse tasks with instructions. However …

Large Language Models (LLMs) can adapt to new tasks via in-context learning (ICL). ICL is
efficient as it does not require any parameter updates to the trained LLM, but only few …

Large language models (LLMs) have demonstrated remarkable capabilities in various tasks.
However, their suitability for domain-specific tasks, is limited due to their immense scale at …

Multimodal large language models (MLLMs) are flourishing, but mainly focus on images with
less attention than videos, especially in sub-fields such as prompt engineering, video chain …

Diffusion models excel at modeling complex data distributions, including those of images,
proteins, and small molecules. However, in many cases, our goal is to model parts of the …

Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects
informative unlabeled data for human annotation, aiming to improve over random sampling …