Search Results for author:
Found 7 papers, 2 papers with code
IAG: Induction-Augmented Generation Framework for Answering Reasoning Questions
In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning.
Zero-Shot Continuous Prompt Transfer: Generalizing Task Semantics Across Language Models
Prompt tuning in natural language processing (NLP) has become an increasingly popular method for adapting large language models to specific tasks.
Unsupervised Chunking with Hierarchical RNN
Our approach involves a two-stage training process: pretraining with an unsupervised parser and finetuning on downstream NLP tasks.
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Hyperlinks, which are commonly used in Web pages, have been leveraged for designing pre-training objectives.
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Our experiments on 13 benchmark datasets across five natural language understanding tasks demonstrate the superiority of our method.
Towards More Effective and Economic Sparsely-Activated Model
To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).
YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker
Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.
