Search Results for author:
Found 4 papers, 3 papers with code
Chain-of-Instructions: Compositional Instruction Tuning on Large Language Models
Fine-tuning large language models (LLMs) with a collection of large and diverse instructions has improved the model's generalization to different tasks, even for unseen tasks.
Cluster-Guided Label Generation in Extreme Multi-Label Classification
For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treating "Wikipedia" and "Wiki" as independent and separate labels.
Posterior Calibrated Training on Sentence Classification Tasks
Here we propose an end-to-end training procedure called posterior calibrated (PosCal) training that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities. We show that PosCal not only helps reduce the calibration error but also improve task performance by penalizing drops in performance of both objectives.
Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization
We find that while position exhibits substantial bias in news articles, this is not the case, for example, with academic papers and meeting minutes.
