UnIte: Uncertainty-based Iterative Document Sampling for Domain Adaptation in Information Retrieval
The episode discusses 'UnIte,' a paper from Seoul National University about uncertainty-based iterative document sampling for domain adaptation in information retrieval. The method filters out noisy documents and actively seeks samples where the model is most uncertain about the new domain's knowledge. This approach leads to significant retrieval quality gains while being more efficient.