6 papers
Optimizing Knowledge Integration in Retrieval-Augmented Generation with Self-Selection
Yan Weng, Fengbin Zhu, Tong Ye +3
Retrieval-Augmented Generation (RAG), which integrates external knowledge into Large Language Models (LLMs), has proven effective in enabling LLMs to produce more accurate and reli…
Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization
Tong Ye, Lingfei Wu, Tengfei Ma +5
Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significa…
On the Calibration and Uncertainty with Pólya-Gamma Augmentation for Dialog Retrieval Models
Tong Ye, Shijing Si, Jianzong Wang +3
Deep neural retrieval models have amply demonstrated their power but estimating the reliability of their predictions remains challenging. Most dialog response retrieval models outp…
Efficient Uncertainty Estimation with Gaussian Process for Reliable Dialog Response Retrieval
Tong Ye, Zhitao Li, Jianzong Wang +2
Deep neural networks have achieved remarkable performance in retrieval-based dialogue systems, but they are shown to be ill calibrated. Though basic calibration methods like Monte…
Uncertainty Calibration for Deep Audio Classifiers
Tong Ye, Shijing Si, Jianzong Wang +2
Although deep Neural Networks (DNNs) have achieved tremendous success in audio classification tasks, their uncertainty calibration are still under-explored. A well-calibrated model…
VU-BERT: A Unified framework for Visual Dialog
Tong Ye, Shijing Si, Jianzong Wang +3
The visual dialog task attempts to train an agent to answer multi-turn questions given an image, which requires the deep understanding of interactions between the image and dialog…