119 citations · 189 across the 15 of their papers we have counts for
6 papers · 1 filter
RAVEN: In-Context Learning with Retrieval-Augmented Encoder-Decoder Language Models
Jie Huang, Wei Ping, Peng Xu +3
In this paper, we investigate the in-context learning ability of retrieval-augmented encoder-decoder language models. We first conduct a comprehensive analysis of existing models a…
Unsupervised Open-domain Keyphrase Generation
Lam Thanh Do, Pritom Saha Akash, Kevin Chen-Chuan Chang
In this work, we study the problem of unsupervised open-domain keyphrase generation, where the objective is a keyphrase generation model that can be built without using human-label…
Mastering the ABCDs of Complex Questions: Answer-Based Claim Decomposition for Fine-grained Self-Evaluation
Nishant Balepur, Jie Huang, Samraj Moorjani +2
When answering complex questions, large language models (LLMs) may produce answers that do not satisfy all criteria of the question. While existing self-evaluation techniques aim t…
CCGen: Explainable Complementary Concept Generation in E-Commerce
Jie Huang, Yifan Gao, Zheng Li +7
We propose and study Complementary Concept Generation (CCGen): given a concept of interest, e.g., "Digital Cameras", generating a list of complementary concepts, e.g., 1) Camera Le…
Quantifying Association Capabilities of Large Language Models and Its Implications on Privacy Leakage
Hanyin Shao, Jie Huang, Shen Zheng +1
The advancement of large language models (LLMs) brings notable improvements across various applications, while simultaneously raising concerns about potential private data exposure…
Expository Text Generation: Imitate, Retrieve, Paraphrase
Nishant Balepur, Jie Huang, Kevin Chen-Chuan Chang
Expository documents are vital resources for conveying complex information to readers. Despite their usefulness, writing expository text by hand is a challenging process that requi…