8 citations · 18 across the 15 of their papers we have counts for
15 papers
Dynamic Attention-Guided Context Decoding for Mitigating Context Faithfulness Hallucinations in Large Language Models
Yanwen Huang, Yong Zhang, Ning Cheng +3
Large language models (LLMs) often exhibit Context Faithfulness Hallucinations, where outputs deviate from retrieved information due to incomplete context integration. Our analysis…
Self-Enhanced Reasoning Training: Activating Latent Reasoning in Small Models for Enhanced Reasoning Distillation
Yong Zhang, Bingyuan Zhang, Zhitao Li +7
The rapid advancement of large language models (LLMs) has significantly enhanced their reasoning abilities, enabling increasingly complex tasks. However, these capabilities often d…
GRASP: Replace Redundant Layers with Adaptive Singular Parameters for Efficient Model Compression
Kainan Liu, Yong Zhang, Ning Cheng +3
Recent studies have demonstrated that many layers are functionally redundant in large language models (LLMs), enabling model compression by removing these layers to reduce inferenc…
PFID: Privacy First Inference Delegation Framework for LLMs
Haoyan Yang, Zhitao Li, Yong Zhang +4
This paper introduces a novel privacy-preservation framework named PFID for LLMs that addresses critical privacy concerns by localizing user data through model sharding and singula…
QLSC: A Query Latent Semantic Calibrator for Robust Extractive Question Answering
Sheng Ouyang, Jianzong Wang, Yong Zhang +5
Extractive Question Answering (EQA) in Machine Reading Comprehension (MRC) often faces the challenge of dealing with semantically identical but format-variant inputs. Our work intr…
Superfiltering: Weak-to-Strong Data Filtering for Fast Instruction-Tuning
Ming Li, Yong Zhang, Shwai He +5
Instruction tuning is critical to improve LLMs but usually suffers from low-quality and redundant data. Data filtering for instruction tuning has proved important in improving both…
Leveraging Biases in Large Language Models: "bias-kNN'' for Effective Few-Shot Learning
Yong Zhang, Hanzhang Li, Zhitao Li +4
Large Language Models (LLMs) have shown significant promise in various applications, including zero-shot and few-shot learning. However, their performance can be hampered by inhere…
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…
Self-Attention for Incomplete Utterance Rewriting
Yong Zhang, Zhitao Li, Jianzong Wang +2
Incomplete utterance rewriting (IUR) has recently become an essential task in NLP, aiming to complement the incomplete utterance with sufficient context information for comprehensi…
Towards ML Engineering: A Brief History Of TensorFlow Extended (TFX)
Konstantinos, Katsiapis, Abhijit Karmarkar +17
Software Engineering, as a discipline, has matured over the past 5+ decades. The modern world heavily depends on it, so the increased maturity of Software Engineering was an eventu…