125 citations · 228 across the 9 of their papers we have counts for
11 papers
Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues?
Bo-Ru Lu, Nikita Haduong, Chia-Hsuan Lee +7
The capabilities of pretrained language models have opened opportunities to explore new application areas, but applications involving human-human interaction are limited by the fac…
Generating Data for Symbolic Language with Large Language Models
Jiacheng Ye, Chengzu Li, Lingpeng Kong +1
While large language models (LLMs) bring not only performance but also complexity, recent work has started to turn LLMs into data generators rather than task inferencers, where ano…
Automated Self-Supervised Learning for Recommendation
Lianghao Xia, Chao Huang, Chunzhen Huang +3
Graph neural networks (GNNs) have emerged as the state-of-the-art paradigm for collaborative filtering (CF). To improve the representation quality over limited labeled data, contra…
Compositional Exemplars for In-context Learning
Jiacheng Ye, Zhiyong Wu, Jiangtao Feng +2
Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task via a prompt consisting of input-output…
Batch Prompting: Efficient Inference with Large Language Model APIs
Zhoujun Cheng, Jungo Kasai, Tao Yu
Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch pr…
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback
Jiacheng Ye, Jiahui Gao, Jiangtao Feng +3
Recently, dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language model…
In-Context Learning for Few-Shot Dialogue State Tracking
Yushi Hu, Chia-Hsuan Lee, Tianbao Xie +3
Collecting and annotating task-oriented dialogues is time-consuming and costly; thus, zero and few shot learning could greatly benefit dialogue state tracking (DST). In this work,…
ZeroGen: Efficient Zero-shot Learning via Dataset Generation
Jiacheng Ye, Jiahui Gao, Qintong Li +5
There is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible a…
End-to-End Cross-Domain Text-to-SQL Semantic Parsing with Auxiliary Task
Peng Shi, Tao Yu, Patrick Ng +1
In this work, we focus on two crucial components in the cross-domain text-to-SQL semantic parsing task: schema linking and value filling. To encourage the model to learn better enc…
Online Conversation Disentanglement with Pointer Networks
Tao Yu, Shafiq Joty
Huge amounts of textual conversations occur online every day, where multiple conversations take place concurrently. Interleaved conversations lead to difficulties in not only follo…
Salvaging Federated Learning by Local Adaptation
Tao Yu, Eugene Bagdasaryan, Vitaly Shmatikov
Federated learning (FL) is a heavily promoted approach for training ML models on sensitive data, e.g., text typed by users on their smartphones. FL is expressly designed for traini…