413 citations · 694 across the 25 of their papers we have counts for
25 papers
LLM-guided Hierarchical Search for End-to-end Reasoning Intensive Retrieval
Nilesh Gupta, Wei-Cheng Chang, Ngot Bui +2
Search systems are increasingly used for reasoning-intensive queries, where what makes a document relevant requires understanding or reasoning over the query-document relation rath…
Retrieval-augmented Encoders for Extreme Multi-label Text Classification
Yau-Shian Wang, Wei-Cheng Chang, Jyun-Yu Jiang +3
Extreme multi-label classification (XMC) seeks to find relevant labels from an extremely large label collection for a given text input. To tackle such a vast label space, current s…
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
Wei-Cheng Chang, Jyun-Yu Jiang, Jiong Zhang +5
Embedding-based Retrieval Models (ERMs) have emerged as a promising framework for large-scale text retrieval problems due to powerful large language models. Nevertheless, fine-tuni…
MinPrompt: Graph-based Minimal Prompt Data Augmentation for Few-shot Question Answering
Xiusi Chen, Jyun-Yu Jiang, Wei-Cheng Chang +3
Recent advances in few-shot question answering (QA) mostly rely on the power of pre-trained large language models (LLMs) and fine-tuning in specific settings. Although the pre-trai…
PINA: Leveraging Side Information in eXtreme Multi-label Classification via Predicted Instance Neighborhood Aggregation
Eli Chien, Jiong Zhang, Cho-Jui Hsieh +4
The eXtreme Multi-label Classification~(XMC) problem seeks to find relevant labels from an exceptionally large label space. Most of the existing XMC learners focus on the extractio…
Uncertainty in Extreme Multi-label Classification
Jyun-Yu Jiang, Wei-Cheng Chang, Jiong Zhong +2
Uncertainty quantification is one of the most crucial tasks to obtain trustworthy and reliable machine learning models for decision making. However, most research in this domain ha…