Search Results for author: Zhicheng Dou

Found 60 papers, 32 papers with code

基于双星型自注意力网络的搜索结果多样化方法(Search Result Diversification Framework Based on Dual Star-shaped Self-Attention Network)

no code implementations CCL 2021 Xubo Qin, Zhicheng Dou, Yutao Zhu, JiRong Wen

“相关研究指出, 用户提交给搜索引擎的查询通常为短查询。由于自然语言本身的特点, 短查询通常具有歧义性, 同一个查询可以指代不同的事物, 或同一事物的不同方面。为了让搜索结果尽可能满足用户多样化的信息需求, 搜索引擎需要对返回的结果进行多样化排序, 搜索结果多样化技术应运而生。目前已有的基于全局交互的多样化方法通过全连接的自注意力网络捕获全体候选文档间的交互关系, 取得了较好的效果。但由于此类方法只考虑文档间的相关关系, 并没有考虑到文档是否具有跟查询相关的有效信息, 在训练数据有限的条件下效率相对较低。该文提出了一种基于双星型自注意力网络的搜索结果多样化方法, 将全连接结构改为星型拓扑结构, 并嵌入查询信息以高效率地提取文档跟查询相关的全局交互特征。相关实验结果显示, 该模型相对于基于全连接自注意力网络的多样化方法, 具备显著的性能优势。”

UFO: a Unified and Flexible Framework for Evaluating Factuality of Large Language Models

1 code implementation22 Feb 2024 Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Ji-Rong Wen

To address these challenges, we categorize four available fact sources: human-written evidence, reference documents, search engine results, and LLM knowledge, along with five text generation tasks containing six representative datasets.

Hallucination Retrieval +1

Interpreting Conversational Dense Retrieval by Rewriting-Enhanced Inversion of Session Embedding

no code implementations20 Feb 2024 Yiruo Cheng, Kelong Mao, Zhicheng Dou

Such transformation is achieved by training a recently proposed Vec2Text model based on the ad-hoc query encoder, leveraging the fact that the session and query embeddings share the same space in existing conversational dense retrieval.

Conversational Search Retrieval

Small Models, Big Insights: Leveraging Slim Proxy Models To Decide When and What to Retrieve for LLMs

1 code implementation19 Feb 2024 Jiejun Tan, Zhicheng Dou, Yutao Zhu, Peidong Guo, Kun Fang, Ji-Rong Wen

The integration of large language models (LLMs) and search engines represents a significant evolution in knowledge acquisition methodologies.

Question Answering

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

Enhancing Multi-field B2B Cloud Solution Matching via Contrastive Pre-training

no code implementations11 Feb 2024 Haonan Chen, Zhicheng Dou, Xuetong Hao, Yunhao Tao, Shiren Song, Zhenli Sheng

Cloud solutions have gained significant popularity in the technology industry as they offer a combination of services and tools to tackle specific problems.

Data Augmentation

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

Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis

no code implementations16 Jan 2024 Zhicheng Dou, Yuchen Guo, Ching-Chun Chang, Huy H. Nguyen, Isao Echizen

In this paper, we present a comprehensive analysis of the impact of prompts on the text generated by LLMs and highlight the potential lack of robustness in one of the current state-of-the-art GPT detectors.

INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning

1 code implementation12 Jan 2024 Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zhicheng Dou, Zheng Liu, Ji-Rong Wen

Despite this, their application to information retrieval (IR) tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language.

document understanding Information Retrieval +2

Soaring from 4K to 400K: Extending LLM's Context with Activation Beacon

1 code implementation7 Jan 2024 Peitian Zhang, Zheng Liu, Shitao Xiao, Ninglu Shao, Qiwei Ye, Zhicheng Dou

Although the context window can be extended through fine-tuning, it will result in a considerable cost at both training and inference time, and exert an unfavorable impact to the LLM's original capabilities.

Language Modelling

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

Retrieve Anything To Augment Large Language Models

1 code implementation11 Oct 2023 Peitian Zhang, Shitao Xiao, Zheng Liu, Zhicheng Dou, Jian-Yun Nie

On the other hand, the task-specific retrievers lack the required versatility, hindering their performance across the diverse retrieval augmentation scenarios.

Knowledge Distillation Retrieval

Optimizing Factual Accuracy in Text Generation through Dynamic Knowledge Selection

no code implementations30 Aug 2023 Hongjin Qian, Zhicheng Dou, Jiejun Tan, Haonan Chen, Haoqi Gu, Ruofei Lai, Xinyu Zhang, Zhao Cao, Ji-Rong Wen

Previous methods use external knowledge as references for text generation to enhance factuality but often struggle with the knowledge mix-up(e. g., entity mismatch) of irrelevant references.

Text Generation

Large Language Models for Information Retrieval: A Survey

1 code implementation14 Aug 2023 Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Haonan Chen, Zhicheng Dou, Ji-Rong Wen

This evolution requires a combination of both traditional methods (such as term-based sparse retrieval methods with rapid response) and modern neural architectures (such as language models with powerful language understanding capacity).

Information Retrieval Question Answering +2

RETA-LLM: A Retrieval-Augmented Large Language Model Toolkit

1 code implementation8 Jun 2023 Jiongnan Liu, Jiajie Jin, Zihan Wang, Jiehan Cheng, Zhicheng Dou, Ji-Rong Wen

To support research in this area and facilitate the development of retrieval-augmented LLM systems, we develop RETA-LLM, a {RET}reival-{A}ugmented LLM toolkit.

Answer Generation Fact Checking +5

User Behavior Simulation with Large Language Model based Agents

1 code implementation5 Jun 2023 Lei Wang, Jingsen Zhang, Hao Yang, ZhiYuan Chen, Jiakai Tang, Zeyu Zhang, Xu Chen, Yankai Lin, Ruihua Song, Wayne Xin Zhao, Jun Xu, Zhicheng Dou, Jun Wang, Ji-Rong Wen

Simulating high quality user behavior data has always been a fundamental problem in human-centered applications, where the major difficulty originates from the intricate mechanism of human decision process.

Language Modelling Large Language Model +2

JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions

1 code implementation24 May 2023 Jiongnan Liu, Zhicheng Dou, Guoyu Tang, Sulong Xu

To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products.

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

WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus

1 code implementation10 Apr 2023 Hongjing Qian, Yutao Zhu, Zhicheng Dou, Haoqi Gu, Xinyu Zhang, Zheng Liu, Ruofei Lai, Zhao Cao, Jian-Yun Nie, Ji-Rong Wen

In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web.

Retrieval Text Generation

CDSM: Cascaded Deep Semantic Matching on Textual Graphs Leveraging Ad-hoc Neighbor Selection

1 code implementation30 Nov 2022 Jing Yao, Zheng Liu, Junhan Yang, Zhicheng Dou, Xing Xie, Ji-Rong Wen

In the first stage, a lightweight CNN-based ad-hod neighbor selector is deployed to filter useful neighbors for the matching task with a small computation cost.

MCP: Self-supervised Pre-training for Personalized Chatbots with Multi-level Contrastive Sampling

no code implementations17 Oct 2022 Zhaoheng Huang, Zhicheng Dou, Yutao Zhu, Zhengyi Ma

To tackle these problems, we propose a self-supervised learning framework MCP for capturing better representations from users' dialogue history for personalized chatbots.

Response Generation Self-Supervised Learning

Hybrid Inverted Index Is a Robust Accelerator for Dense Retrieval

1 code implementation11 Oct 2022 Peitian Zhang, Zheng Liu, Shitao Xiao, Zhicheng Dou, Jing Yao

Based on comprehensive experiments on popular retrieval benchmarks, we verify that clusters and terms indeed complement each other, enabling HI$^2$ to achieve lossless retrieval quality with competitive efficiency across various index settings.

Knowledge Distillation Quantization +1

Enhancing User Behavior Sequence Modeling by Generative Tasks for Session Search

1 code implementation23 Aug 2022 Haonan Chen, Zhicheng Dou, Yutao Zhu, Zhao Cao, Xiaohua Cheng, Ji-Rong Wen

To help the encoding of the current user behavior sequence, we propose to use a decoder and the information of future sequences and a supplemental query.

Session Search

From Easy to Hard: A Dual Curriculum Learning Framework for Context-Aware Document Ranking

1 code implementation22 Aug 2022 Yutao Zhu, Jian-Yun Nie, Yixuan Su, Haonan Chen, Xinyu Zhang, Zhicheng Dou

In this work, we propose a curriculum learning framework for context-aware document ranking, in which the ranking model learns matching signals between the search context and the candidate document in an easy-to-hard manner.

Document Ranking

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

KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models

no code implementations28 Feb 2022 Daniel Gao, Yantao Jia, Lei LI, Chengzhen Fu, Zhicheng Dou, Hao Jiang, Xinyu Zhang, Lei Chen, Zhao Cao

However, to figure out whether PLMs can be reliable knowledge sources and used as alternative knowledge bases (KBs), we need to further explore some critical features of PLMs.

General Knowledge Memorization +1

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

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

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

Towards More Effective and Economic Sparsely-Activated Model

no code implementations14 Oct 2021 Hao Jiang, Ke Zhan, Jianwei Qu, Yongkang Wu, Zhaoye Fei, Xinyu Zhang, Lei Chen, Zhicheng Dou, Xipeng Qiu, Zikai Guo, Ruofei Lai, Jiawen Wu, Enrui Hu, Yinxia Zhang, Yantao Jia, Fan Yu, Zhao Cao

To increase the number of activated experts without an increase in computational cost, we propose SAM (Switch and Mixture) routing, an efficient hierarchical routing mechanism that activates multiple experts in a same device (GPU).

Learning to Select Historical News Articles for Interaction based Neural News Recommendation

no code implementations13 Oct 2021 Peitian Zhang, Zhicheng Dou, Jing Yao

The key to personalized news recommendation is to match the user's interests with the candidate news precisely and efficiently.

News Recommendation

YES SIR!Optimizing Semantic Space of Negatives with Self-Involvement Ranker

no code implementations14 Sep 2021 Ruizhi Pu, Xinyu Zhang, Ruofei Lai, Zikai Guo, Yinxia Zhang, Hao Jiang, Yongkang Wu, Yantao Jia, Zhicheng Dou, Zhao Cao

Finally, supervisory signal in rear compressor is computed based on condition probability and thus can control sample dynamic and further enhance the model performance.

Document Ranking Information Retrieval +1

Contrastive Learning of User Behavior Sequence for Context-Aware Document Ranking

1 code implementation24 Aug 2021 Yutao Zhu, Jian-Yun Nie, Zhicheng Dou, Zhengyi Ma, Xinyu Zhang, Pan Du, Xiaochen Zuo, Hao Jiang

To learn a more robust representation of the user behavior sequence, we propose a method based on contrastive learning, which takes into account the possible variations in user's behavior sequences.

Contrastive Learning Data Augmentation +1

Pre-training for Ad-hoc Retrieval: Hyperlink is Also You Need

1 code implementation20 Aug 2021 Zhengyi Ma, Zhicheng Dou, Wei Xu, Xinyu Zhang, Hao Jiang, Zhao Cao, Ji-Rong Wen

In this paper, we propose to leverage the large-scale hyperlinks and anchor texts to pre-train the language model for ad-hoc retrieval.

Language Modelling Retrieval

One Chatbot Per Person: Creating Personalized Chatbots based on Implicit User Profiles

1 code implementation20 Aug 2021 Zhengyi Ma, Zhicheng Dou, Yutao Zhu, Hanxun Zhong, Ji-Rong Wen

Specifically, leveraging the benefits of Transformer on language understanding, we train a personalized language model to construct a general user profile from the user's historical responses.

Chatbot Language Modelling

Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot

1 code implementation18 Aug 2021 Hongjin Qian, Zhicheng Dou, Yutao Zhu, Yueyuan Ma, Ji-Rong Wen

To learn a user's personalized language style, we elaborately build language models from shallow to deep using the user's historical responses; To model a user's personalized preferences, we explore the conditional relations underneath each post-response pair of the user.

Chatbot Retrieval

Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals

1 code implementation18 Jul 2021 Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, Hao Jiang, Zhicheng Dou

The final response is selected according to the predicted knowledge, the goal to achieve, and the context.

Multi-Task Learning Retrieval

Answer Complex Questions: Path Ranker Is All You Need

3 code implementations Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021 Xinyu Zhang, Ke Zhan, Enrui Hu, Chengzhen Fu, Lan Luo, Hao Jiang, Yantao Jia, Fan Yu, Zhicheng Dou, Zhao Cao, Lei Chen

Currently, the most popular method for open-domain Question Answering (QA) adopts "Retriever and Reader" pipeline, where the retriever extracts a list of candidate documents from a large set of documents followed by a ranker to rank the most relevant documents and the reader extracts answer from the candidates.

Open-Domain Question Answering

Emotion Eliciting Machine: Emotion Eliciting Conversation Generation based on Dual Generator

no code implementations18 May 2021 Hao Jiang, Yutao Zhu, Xinyu Zhang, Zhicheng Dou, Pan Du, Te Pi, Yantao Jia

Then we propose a dual encoder-decoder structure to model the generation of responses in both positive and negative side based on the changes of the user's emotion status in the conversation.

Neural Sentence Ordering Based on Constraint Graphs

1 code implementation27 Jan 2021 Yutao Zhu, Kun Zhou, Jian-Yun Nie, Shengchao Liu, Zhicheng Dou

Our experiments on five benchmark datasets show that our method outperforms all the existing baselines significantly, achieving a new state-of-the-art performance.

Sentence Sentence Ordering

Content Selection Network for Document-grounded Retrieval-based Chatbots

1 code implementation21 Jan 2021 Yutao Zhu, Jian-Yun Nie, Kun Zhou, Pan Du, Zhicheng Dou

It is thus crucial to select the part of document content relevant to the current conversation context.

Retrieval

Pchatbot: A Large-Scale Dataset for Personalized Chatbot

2 code implementations28 Sep 2020 Hongjin Qian, Xiaohe Li, Hanxun Zhong, Yu Guo, Yueyuan Ma, Yutao Zhu, Zhanliang Liu, Zhicheng Dou, Ji-Rong Wen

This enables the development of personalized dialogue models that directly learn implicit user personality from the user's dialogue history.

Chatbot

ScriptWriter: Narrative-Guided Script Generation

1 code implementation ACL 2020 Yutao Zhu, Ruihua Song, Zhicheng Dou, Jian-Yun Nie, Jin Zhou

In dialogue systems, it would also be useful to drive dialogues by a dialogue plan.

Personalizing Search Results Using Hierarchical RNN with Query-aware Attention

no code implementations20 Aug 2019 Songwei Ge, Zhicheng Dou, Zhengbao Jiang, Jian-Yun Nie, Ji-Rong Wen

Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.

Attribute

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