5 accepted papers
From content moderation to content curation, applications requiring vision classifiers for visual concepts are rapidly expanding. Existing human-in-the-loop approaches typically assume users begin with a clear, stable concept understanding to be able to provide high-quality supervision. In reality,
Due to the size and complexity of modern large language models (LLMs), it has proven challenging to uncover the underlying mechanisms that models use to solve reasoning problems. For instance, is their reasoning for a specific problem localized to certain parts of the network? Do they break down the…
From content moderation to wildlife conservation the number of applications that require models to recognize nuanced or subjective visual concepts is growing. Traditionally developing classifiers for such concepts requires substantial manual effort measured in hours days or even months to identify a…
Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space recognizing instruments and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an e…
Label noise is increasingly prevalent in datasets acquired from noisy channels. Existing approaches that detect and remove label noise generally rely on some form of supervision, which is not scalable and error-prone. In this paper, we propose NoiseRank, for unsupervised label noise reduction using…