Search Results for author:
Found 4 papers, 3 papers with code
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
While copious solutions, such as active learning for small language models (SLMs) and prevalent in-context learning in the era of large language models (LLMs), have been proposed and alleviate the labeling burden to some extent, their performances are still subject to human intervention.
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works.
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.
