Search Results for author: Yujia Zhou

Found 19 papers, 7 papers with code

Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models

no code implementations11 Mar 2024 Weihang Su, Changyue Wang, Qingyao Ai, Yiran Hu, Zhijing Wu, Yujia Zhou, Yiqun Liu

Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate.

Hallucination

BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence

no code implementations19 Feb 2024 Jiajie Jin, Yutao Zhu, Yujia Zhou, Zhicheng Dou

Retrieval-augmented large language models (LLMs) have demonstrated efficacy in knowledge-intensive tasks such as open-domain QA, addressing inherent challenges in knowledge update and factual inadequacy.

Question Answering Retrieval

Metacognitive Retrieval-Augmented Large Language Models

1 code implementation18 Feb 2024 Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie, Zhicheng Dou

Drawing from cognitive psychology, metacognition allows an entity to self-reflect and critically evaluate its cognitive processes.

Response Generation Retrieval

Cognitive Personalized Search Integrating Large Language Models with an Efficient Memory Mechanism

no code implementations16 Feb 2024 Yujia Zhou, Qiannan Zhu, Jiajie Jin, Zhicheng Dou

To counter this limitation, personalized search has been developed to re-rank results based on user preferences derived from query logs.

Grounding Language Model with Chunking-Free In-Context Retrieval

no code implementations15 Feb 2024 Hongjin Qian, Zheng Liu, Kelong Mao, Yujia Zhou, Zhicheng Dou

These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained.

Chunking Language Modelling +2

Towards a Unified Language Model for Knowledge-Intensive Tasks Utilizing External Corpus

no code implementations2 Feb 2024 Xiaoxi Li, Zhicheng Dou, Yujia Zhou, Fangchao Liu

Through generative retrieval (GR) approach, language models can achieve superior retrieval performance by directly generating relevant document identifiers (DocIDs).

Language Modelling Retrieval

UniGen: A Unified Generative Framework for Retrieval and Question Answering with Large Language Models

no code implementations18 Dec 2023 Xiaoxi Li, Yujia Zhou, Zhicheng Dou

Generative information retrieval, encompassing two major tasks of Generative Document Retrieval (GDR) and Grounded Answer Generation (GAR), has gained significant attention in the area of information retrieval and natural language processing.

Answer Generation Information Retrieval +2

Term-Sets Can Be Strong Document Identifiers For Auto-Regressive Search Engines

no code implementations23 May 2023 Peitian Zhang, Zheng Liu, Yujia Zhou, Zhicheng Dou, Zhao Cao

With AutoTSG, any permutation of the term-set identifier will lead to the retrieval of the corresponding document, thus largely relaxing the requirement of exact generation.

Information Retrieval Natural Questions +1

Ultron: An Ultimate Retriever on Corpus with a Model-based Indexer

no code implementations19 Aug 2022 Yujia Zhou, Jing Yao, Zhicheng Dou, Ledell Wu, Peitian Zhang, Ji-Rong Wen

In order to unify these two stages, we explore a model-based indexer for document retrieval.

Retrieval

Socialformer: Social Network Inspired Long Document Modeling for Document Ranking

1 code implementation22 Feb 2022 Yujia Zhou, Zhicheng Dou, Huaying Yuan, Zhengyi Ma

In this paper, we propose the model Socialformer, which introduces the characteristics of social networks into designing sparse attention patterns for long document modeling in document ranking.

Document Ranking

Group based Personalized Search by Integrating Search Behaviour and Friend Network

1 code implementation24 Nov 2021 Yujia Zhou, Zhicheng Dou, Bingzheng Wei, Ruobing Xievand Ji-Rong Wen

Specifically, we propose a friend network enhanced personalized search model, which groups the user into multiple friend circles based on search behaviours and friend relations respectively.

Re-Ranking

PSSL: Self-supervised Learning for Personalized Search with Contrastive Sampling

1 code implementation24 Nov 2021 Yujia Zhou, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen

Personalized search plays a crucial role in improving user search experience owing to its ability to build user profiles based on historical behaviors.

Self-Supervised Learning Sentence

Simple Recurrent Neural Networks is all we need for clinical events predictions using EHR data

1 code implementation3 Oct 2021 Laila Rasmy, Jie Zhu, Zhiheng Li, Xin Hao, Hong Thoai Tran, Yujia Zhou, Firat Tiryaki, Yang Xiang, Hua Xu, Degui Zhi

As a result, deep learning models developed for sequence modeling, like recurrent neural networks (RNNs) are common architecture for EHR-based clinical events predictive models.

Bayesian Optimization

Numerical Simulation of Bubbly Flow Using Partially Averaged Navier−Stokes Simulation and a Path Oscillation Model in the Euler−Lagrange Approach

no code implementations journal 2021 Yujia Zhou, Chenru Zhao, Bingqiang Ji, and Hanliang Bo

Large bubbles always undergo asymmetric paths due to the wake instability, which was rarely considered in the simulation of bubbly flow in complex flow systems such as bubble columns.

COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model

no code implementations13 Jul 2020 Jingqi Wang, Noor Abu-el-rub, Josh Gray, Huy Anh Pham, Yujia Zhou, Frank Manion, Mei Liu, Xing Song, Hua Xu, Masoud Rouhizadeh, Yaoyun Zhang

To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text.

Negation

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