Papers by Rohit Babbar

1 papers
Large Language Model as a Teacher for Zero-shot Tagging at Extreme Scales (2025.coling-main)

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Challenge: Extreme Zero-shot XMC uses lightweight bi-encoders to identify pseudo labels . state-of-the-art methods rely on suboptimal labels for training .
Approach: They propose a framework that uses a lightweight bi-encoder to identify high-quality pseudo labels during training while employing a lightweight bi-coder for efficient inference.
Outcome: The proposed framework achieves superior performance and efficiency over existing methods.

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