58 citations · 150 across the 81 of their papers we have counts for
6 papers · 2 filters
PRCA: Fitting Black-Box Large Language Models for Retrieval Question Answering via Pluggable Reward-Driven Contextual Adapter
Haoyan Yang, Zhitao Li, Yong Zhang +4
The Retrieval Question Answering (ReQA) task employs the retrieval-augmented framework, composed of a retriever and generator. The generator formulates the answer based on the docu…
Boosting Chinese ASR Error Correction with Dynamic Error Scaling Mechanism
Jiaxin Fan, Yong Zhang, Hanzhang Li +5
Chinese Automatic Speech Recognition (ASR) error correction presents significant challenges due to the Chinese language's unique features, including a large character set and borde…
Prompt Guided Copy Mechanism for Conversational Question Answering
Yong Zhang, Zhitao Li, Jianzong Wang +4
Conversational Question Answering (CQA) is a challenging task that aims to generate natural answers for conversational flow questions. In this paper, we propose a pluggable approac…
From Quantity to Quality: Boosting LLM Performance with Self-Guided Data Selection for Instruction Tuning
Ming Li, Yong Zhang, Zhitao Li +6
In the realm of Large Language Models (LLMs), the balance between instruction data quality and quantity is a focal point. Recognizing this, we introduce a self-guided methodology f…
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…