145 citations · 265 across the 18 of their papers we have counts for
9 papers · 1 filter
XRAG: eXamining the Core -- Benchmarking Foundational Components in Advanced Retrieval-Augmented Generation
Qili Zhang, Qianren Mao, Yangyifei Luo +15
Retrieval-augmented generation (RAG) synergizes the retrieval of pertinent data with the generative capabilities of Large Language Models (LLMs), ensuring that the generated output…
Narrowing the Gap between Supervised and Unsupervised Sentence Representation Learning with Large Language Model
Mingxin Li, Richong Zhang, Zhijie Nie +1
Sentence Representation Learning (SRL) is a fundamental task in Natural Language Processing (NLP), with the Contrastive Learning of Sentence Embeddings (CSE) being the mainstream t…
Code-Style In-Context Learning for Knowledge-Based Question Answering
Zhijie Nie, Richong Zhang, Zhongyuan Wang +1
Current methods for Knowledge-Based Question Answering (KBQA) usually rely on complex training techniques and model frameworks, leading to many limitations in practical application…
Parallel Interactive Networks for Multi-Domain Dialogue State Generation
Junfan Chen, Richong Zhang, Yongyi Mao +1
The dependencies between system and user utterances in the same turn and across different turns are not fully considered in existing multidomain dialogue state tracking (MDST) mode…
Neural Dialogue State Tracking with Temporally Expressive Networks
Junfan Chen, Richong Zhang, Yongyi Mao +1
Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to exp…
On SkipGram Word Embedding Models with Negative Sampling: Unified Framework and Impact of Noise Distributions
Dezhi Liu, Richong Zhang, Ziqiao Wang
SkipGram word embedding models with negative sampling, or SGN in short, is an elegant family of word embedding models. In this paper, we formulate a framework for word embedding, r…
Recurrent Interaction Network for Jointly Extracting Entities and Classifying Relations
Kai Sun, Richong Zhang, Samuel Mensah +2
The idea of using multi-task learning approaches to address the joint extraction of entity and relation is motivated by the relatedness between the entity recognition task and the…
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework
Junfan Chen, Richong Zhang, Yongyi Mao +2
Distant supervision for relation extraction enables one to effectively acquire structured relations out of very large text corpora with less human efforts. Nevertheless, most of th…
Augmenting Data with Mixup for Sentence Classification: An Empirical Study
Hongyu Guo, Yongyi Mao, Richong Zhang
Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly imp…