Search Results for author: Zhaochun Ren

Found 81 papers, 53 papers with code

Neural Rating Regression with Abstractive Tips Generation for Recommendation

no code implementations1 Aug 2017 Piji Li, ZiHao Wang, Zhaochun Ren, Lidong Bing, Wai Lam

In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings.

regression Sentence

Neural Att entive Session-based Recommendation

1 code implementation CIKM 2017 Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma

Specifically, we explore a hybrid encoder with an attention mechanism to model the user’s sequential behavior and capture the user’s main purpose in the current session, which are combined as a unified session representation later.

Session-Based Recommendations

Neural Attentive Session-based Recommendation

3 code implementations13 Nov 2017 Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma

Specifically, we explore a hybrid encoder with an attention mechanism to model the user's sequential behavior and capture the user's main purpose in the current session, which are combined as a unified session representation later.

Session-Based Recommendations

Knowledge Diffusion for Neural Dialogue Generation

1 code implementation ACL 2018 Shuman Liu, Hongshen Chen, Zhaochun Ren, Yang Feng, Qun Liu, Dawei Yin

Our empirical study on a real-world dataset prove that our model is capable of generating meaningful, diverse and natural responses for both factoid-questions and knowledge grounded chi-chats.

Dialogue Generation Question Answering +1

Explicit State Tracking with Semi-Supervision for Neural Dialogue Generation

2 code implementations31 Aug 2018 Xisen Jin, Wenqiang Lei, Zhaochun Ren, Hongshen Chen, Shangsong Liang, Yihong Zhao, Dawei Yin

However, the \emph{expensive nature of state labeling} and the \emph{weak interpretability} make the dialogue state tracking a challenging problem for both task-oriented and non-task-oriented dialogue generation: For generating responses in task-oriented dialogues, state tracking is usually learned from manually annotated corpora, where the human annotation is expensive for training; for generating responses in non-task-oriented dialogues, most of existing work neglects the explicit state tracking due to the unlimited number of dialogue states.

Dialogue Generation Dialogue State Tracking

Streaming Graph Neural Networks

2 code implementations24 Oct 2018 Yao Ma, Ziyi Guo, Zhaochun Ren, Eric Zhao, Jiliang Tang, Dawei Yin

Current graph neural network models cannot utilize the dynamic information in dynamic graphs.

Community Detection General Classification +3

RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation

1 code implementation6 Dec 2018 Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, Maarten de Rijke

RepeatNet integrates a regular neural recommendation approach in the decoder with a new repeat recommendation mechanism that can choose items from a user's history and recommends them at the right time.

Session-Based Recommendations

Abstractive Text Summarization by Incorporating Reader Comments

no code implementations13 Dec 2018 Shen Gao, Xiuying Chen, Piji Li, Zhaochun Ren, Lidong Bing, Dongyan Zhao, Rui Yan

To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect.

Reader-Aware Summarization

Product-Aware Answer Generation in E-Commerce Question-Answering

1 code implementation23 Jan 2019 Shen Gao, Zhaochun Ren, Yihong Eric Zhao, Dongyan Zhao, Dawei Yin, Rui Yan

In this paper, we propose the task of product-aware answer generation, which tends to generate an accurate and complete answer from large-scale unlabeled e-commerce reviews and product attributes.

Answer Generation Question Answering

Improving Outfit Recommendation with Co-supervision of Fashion Generation

no code implementations24 Aug 2019 Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke

FARM improves visual understanding by incorporating the supervision of generation loss, which we hypothesize to be able to better encode aesthetic information.

Parallel Split-Join Networks for Shared-account Cross-domain Sequential Recommendations

no code implementations6 Oct 2019 Wenchao Sun, Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Zhaochun Ren, Jun Ma, Maarten de Rijke

We study sequential recommendation in a particularly challenging context, in which multiple individual users share asingle account (i. e., they have a shared account) and in which user behavior is available in multiple domains (i. e., recommendations are cross-domain).

Sequential Recommendation

EmpDG: Multiresolution Interactive Empathetic Dialogue Generation

1 code implementation20 Nov 2019 Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen

In response to this problem, we propose a multi-resolution adversarial model -- EmpDG, to generate more empathetic responses.

Dialogue Generation

A Neural Topical Expansion Framework for Unstructured Persona-oriented Dialogue Generation

2 code implementations6 Feb 2020 Minghong Xu, Piji Li, Haoran Yang, Pengjie Ren, Zhaochun Ren, Zhumin Chen, Jun Ma

To address this, we propose a neural topical expansion framework, namely Persona Exploration and Exploitation (PEE), which is able to extend the predefined user persona description with semantically correlated content before utilizing them to generate dialogue responses.

Descriptive Dialogue Generation

Conversations with Search Engines: SERP-based Conversational Response Generation

1 code implementation29 Apr 2020 Pengjie Ren, Zhumin Chen, Zhaochun Ren, Evangelos Kanoulas, Christof Monz, Maarten de Rijke

In this paper, we address the problem of answering complex information needs by conversing conversations with search engines, in the sense that users can express their queries in natural language, and directly receivethe information they need from a short system response in a conversational manner.

Conversational Response Generation Conversational Search +1

From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

no code implementations10 May 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words.

Text Summarization

Knowledge Bridging for Empathetic Dialogue Generation

1 code implementation21 Sep 2020 Qintong Li, Piji Li, Zhaochun Ren, Pengjie Ren, Zhumin Chen

Finally, to generate the empathetic response, we propose an emotional cross-attention mechanism to learn the emotional dependencies from the emotional context graph.

Dialogue Generation

Meaningful Answer Generation of E-Commerce Question-Answering

no code implementations14 Nov 2020 Shen Gao, Xiuying Chen, Zhaochun Ren, Dongyan Zhao, Rui Yan

To generate more meaningful answers, in this paper, we propose a novel generative neural model, called the Meaningful Product Answer Generator (MPAG), which alleviates the safe answer problem by taking product reviews, product attributes, and a prototype answer into consideration.

Answer Generation Question Answering +1

Mixed Information Flow for Cross-domain Sequential Recommendations

1 code implementation1 Dec 2020 Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Lifan Zhao, Jun Ma, Maarten de Rijke

One of the key challenges in cross-domain sequential recommendation is to grasp and transfer the flow of information from multiple domains so as to promote recommendations in all domains.

Sequential Recommendation Transfer Learning

EmpDG: Multi-resolution Interactive Empathetic Dialogue Generation

1 code implementation COLING 2020 Qintong Li, Hongshen Chen, Zhaochun Ren, Pengjie Ren, Zhaopeng Tu, Zhumin Chen

In response to this problem, we propose a multi-resolution adversarial model {--} EmpDG, to generate more empathetic responses.

Dialogue Generation

Abstractive Opinion Tagging

1 code implementation18 Jan 2021 Qintong Li, Piji Li, Xinyi Li, Zhaochun Ren, Zhumin Chen, Maarten de Rijke

In this paper, we propose the abstractive opinion tagging task, where systems have to automatically generate a ranked list of opinion tags that are based on, but need not occur in, a given set of user-generated reviews.

Sentence

Wizard of Search Engine: Access to Information Through Conversations with Search Engines

1 code implementation18 May 2021 Pengjie Ren, Zhongkun Liu, Xiaomeng Song, Hongtao Tian, Zhumin Chen, Zhaochun Ren, Maarten de Rijke

(2) We release a benchmark dataset, called wizard of search engine (WISE), which allows for comprehensive and in-depth research on all aspects of CIS.

Intent Detection Keyphrase Extraction +1

Improving Transformer-based Sequential Recommenders through Preference Editing

no code implementations23 Jun 2021 Muyang Ma, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Jun Ma, Maarten de Rijke

By doing so, the SR model is able to learn how to identify common and unique user preferences, and thereby do better user preference extraction and representation.

Self-Supervised Learning Sequential Recommendation

Few-Shot Electronic Health Record Coding through Graph Contrastive Learning

1 code implementation29 Jun 2021 Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Evangelos Kanoulas, Maarten de Rijke

We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task.

Contrastive Learning Few-Shot Learning

Learning to Ask Conversational Questions by Optimizing Levenshtein Distance

1 code implementation ACL 2021 Zhongkun Liu, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Maarten de Rijke, Ming Zhou

Conversational Question Simplification (CQS) aims to simplify self-contained questions into conversational ones by incorporating some conversational characteristics, e. g., anaphora and ellipsis.

ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues

1 code implementation1 Sep 2021 Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen

(1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i. e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues.

Benchmarking Contrastive Learning +2

Membership Inference Attacks Against Recommender Systems

1 code implementation16 Sep 2021 Minxing Zhang, Zhaochun Ren, Zihan Wang, Pengjie Ren, Zhumin Chen, Pengfei Hu, Yang Zhang

In this paper, we make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference.

Recommendation Systems

Metaphorical User Simulators for Evaluating Task-oriented Dialogue Systems

2 code implementations2 Apr 2022 Weiwei Sun, Shuyu Guo, Shuo Zhang, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

Employing existing user simulators to evaluate TDSs is challenging as user simulators are primarily designed to optimize dialogue policies for TDSs and have limited evaluation capabilities.

Task-Oriented Dialogue Systems

Paying More Attention to Self-attention: Improving Pre-trained Language Models via Attention Guiding

no code implementations6 Apr 2022 Shanshan Wang, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Pengjie Ren

In this work, we propose a simple yet effective attention guiding mechanism to improve the performance of PLM by encouraging attention towards the established goals.

Information Retrieval Retrieval

Event Transition Planning for Open-ended Text Generation

1 code implementation Findings (ACL) 2022 Qintong Li, Piji Li, Wei Bi, Zhaochun Ren, Yuxuan Lai, Lingpeng Kong

Open-ended text generation tasks, such as dialogue generation and story completion, require models to generate a coherent continuation given limited preceding context.

Dialogue Generation Story Completion

Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective

no code implementations15 Jun 2022 Xin Xin, Tiago Pimentel, Alexandros Karatzoglou, Pengjie Ren, Konstantina Christakopoulou, Zhaochun Ren

As reinforcement learning (RL) naturally fits this objective -- maximizing an user's reward per session -- it has become an emerging topic in recommender systems.

Recommendation Systems reinforcement-learning +1

Debiasing Learning for Membership Inference Attacks Against Recommender Systems

1 code implementation24 Jun 2022 Zihan Wang, Na Huang, Fei Sun, Pengjie Ren, Zhumin Chen, Hengliang Luo, Maarten de Rijke, Zhaochun Ren

To address the above limitations, we propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components: (1) a difference vector generator, (2) a disentangled encoder, (3) a weight estimator, and (4) an attack model.

Recommendation Systems

On the User Behavior Leakage from Recommender System Exposure

1 code implementation16 Oct 2022 Xin Xin, Jiyuan Yang, Hanbing Wang, Jun Ma, Pengjie Ren, Hengliang Luo, Xinlei Shi, Zhumin Chen, Zhaochun Ren

Given the fact that system exposure data could be widely accessed from a relatively larger scope, we believe that the user past behavior privacy has a high risk of leakage in recommender systems.

Recommendation Systems

DGEKT: A Dual Graph Ensemble Learning Method for Knowledge Tracing

1 code implementation23 Nov 2022 Chaoran Cui, Yumo Yao, Chunyun Zhang, Hebo Ma, Yuling Ma, Zhaochun Ren, Chen Zhang, James Ko

Knowledge tracing aims to trace students' evolving knowledge states by predicting their future performance on concept-related exercises.

Ensemble Learning Knowledge Distillation +1

Feature-Level Debiased Natural Language Understanding

1 code implementation11 Dec 2022 Yougang Lyu, Piji Li, Yechang Yang, Maarten de Rijke, Pengjie Ren, Yukun Zhao, Dawei Yin, Zhaochun Ren

We also propose a dynamic negative sampling strategy to capture the dynamic influence of biases by employing a bias-only model to dynamically select the most similar biased negative samples.

Contrastive Learning Natural Language Understanding

Contrastive Learning Reduces Hallucination in Conversations

1 code implementation20 Dec 2022 Weiwei Sun, Zhengliang Shi, Shen Gao, Pengjie Ren, Maarten de Rijke, Zhaochun Ren

MixCL effectively reduces the hallucination of LMs in conversations and achieves the highest performance among LM-based dialogue agents in terms of relevancy and factuality.

Contrastive Learning Hallucination

Variational Reasoning over Incomplete Knowledge Graphs for Conversational Recommendation

1 code implementation22 Dec 2022 XiaoYu Zhang, Xin Xin, Dongdong Li, Wenxuan Liu, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren

We propose a variational Bayesian method to approximate posterior distributions over dialogue-specific subgraphs, which not only leverages the dialogue corpus for restructuring missing entity relations but also dynamically selects knowledge based on the dialogue context.

Knowledge Graphs Recommendation Systems

Modeling Sequential Recommendation as Missing Information Imputation

1 code implementation4 Jan 2023 Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren

To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.

Imputation Sequential Recommendation

CTRLStruct: Dialogue Structure Learning for Open-Domain Response Generation

1 code implementation2 Mar 2023 Congchi Yin, Piji Li, Zhaochun Ren

Then we perform clustering to utterance-level representations and form topic-level clusters that can be considered as vertices in dialogue structure graph.

Contrastive Learning Dialogue Generation +4

Generative Knowledge Selection for Knowledge-Grounded Dialogues

1 code implementation10 Apr 2023 Weiwei Sun, Pengjie Ren, Zhaochun Ren

However, such approaches neglect the interactions between snippets, leading to difficulties in inferring the meaning of snippets.

Response Generation

Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

1 code implementation9 May 2023 Xin Xin, Xiangyuan Liu, Hanbing Wang, Pengjie Ren, Zhumin Chen, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon Jose, Maarten de Rijke, Zhaochun Ren

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases.

Denoising Open-Ended Question Answering +2

Contrastive State Augmentations for Reinforcement Learning-Based Recommender Systems

1 code implementation18 May 2023 Zhaochun Ren, Na Huang, Yidan Wang, Pengjie Ren, Jun Ma, Jiahuan Lei, Xinlei Shi, Hengliang Luo, Joemon M Jose, Xin Xin

For the second issue, we propose introducing contrastive signals between augmented states and the state randomly sampled from other sessions to improve the state representation learning further.

Recommendation Systems reinforcement-learning +2

UMSE: Unified Multi-scenario Summarization Evaluation

1 code implementation26 May 2023 Shen Gao, Zhitao Yao, Chongyang Tao, Xiuying Chen, Pengjie Ren, Zhaochun Ren, Zhumin Chen

Experimental results across three typical scenarios on the benchmark dataset SummEval indicate that our UMSE can achieve comparable performance with several existing strong methods which are specifically designed for each scenario.

Text Summarization

Towards Explainable Conversational Recommender Systems

1 code implementation27 May 2023 Shuyu Guo, Shuo Zhang, Weiwei Sun, Pengjie Ren, Zhumin Chen, Zhaochun Ren

To achieve this, we conduct manual and automatic approaches to extend these dialogues and construct a new CRS dataset, namely Explainable Recommendation Dialogues (E-ReDial).

Explainable Recommendation Explanation Generation +2

Answering Ambiguous Questions via Iterative Prompting

1 code implementation8 Jul 2023 Weiwei Sun, Hengyi Cai, Hongshen Chen, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

To provide feasible answers to an ambiguous question, one approach is to directly predict all valid answers, but this can struggle with balancing relevance and diversity.

Open-Domain Question Answering valid

Intent-calibrated Self-training for Answer Selection in Open-domain Dialogues

no code implementations13 Jul 2023 Wentao Deng, Jiahuan Pei, Zhaochun Ren, Zhumin Chen, Pengjie Ren

Specifically, it improves 2. 06% and 1. 00% of F1 score on the two datasets, compared with the strongest baseline with only 5% labeled data.

Answer Selection

Confucius: Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum

1 code implementation27 Aug 2023 Shen Gao, Zhengliang Shi, Minghang Zhu, Bowen Fang, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren

Augmenting large language models (LLMs) with external tools has emerged as a promising approach to extending the capability of LLMs.

RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue

no code implementations15 Sep 2023 Zhengliang Shi, Weiwei Sun, Shuo Zhang, Zhen Zhang, Pengjie Ren, Zhaochun Ren

To this end, we propose the Reference-Assisted Dialogue Evaluation (RADE) approach under the multi-task learning framework, which leverages the pre-created utterance as reference other than the gold response to relief the one-to-many problem.

Dialogue Evaluation Multi-Task Learning +1

Generalizing Few-Shot Named Entity Recognizers to Unseen Domains with Type-Related Features

1 code implementation15 Oct 2023 Zihan Wang, Ziqi Zhao, Zhumin Chen, Pengjie Ren, Maarten de Rijke, Zhaochun Ren

To address this limitation, recent studies enable generalization to an unseen target domain with only a few labeled examples using data augmentation techniques.

Data Augmentation few-shot-ner +5

Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method

no code implementations27 Oct 2023 Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Chong Meng, Shuaiqiang Wang, Zhicong Cheng, Zhaochun Ren, Dawei Yin

In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results.

Instruction Distillation Makes Large Language Models Efficient Zero-shot Rankers

1 code implementation2 Nov 2023 Weiwei Sun, Zheng Chen, Xinyu Ma, Lingyong Yan, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren

Furthermore, our approach surpasses the performance of existing supervised methods like monoT5 and is on par with the state-of-the-art zero-shot methods.

Prompt Engineering

Learning Robust Sequential Recommenders through Confident Soft Labels

1 code implementation4 Nov 2023 Shiguang Wu, Xin Xin, Pengjie Ren, Zhumin Chen, Jun Ma, Maarten de Rijke, Zhaochun Ren

CSRec contains a teacher module that generates high-quality and confident soft labels and a student module that acts as the target recommender and is trained on the combination of dense, soft labels and sparse, one-hot labels.

Multi-class Classification Sequential Recommendation

Multi-Defendant Legal Judgment Prediction via Hierarchical Reasoning

1 code implementation10 Dec 2023 Yougang Lyu, Jitai Hao, Zihan Wang, Kai Zhao, Shen Gao, Pengjie Ren, Zhumin Chen, Fang Wang, Zhaochun Ren

Multiple defendants in a criminal fact description generally exhibit complex interactions, and cannot be well handled by existing Legal Judgment Prediction (LJP) methods which focus on predicting judgment results (e. g., law articles, charges, and terms of penalty) for single-defendant cases.

Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure

1 code implementation12 Dec 2023 Jiyuan Yang, Yue Ding, Yidan Wang, Pengjie Ren, Zhumin Chen, Fei Cai, Jun Ma, Rui Zhang, Zhaochun Ren, Xin Xin

Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples.

Sequential Recommendation

On the Effectiveness of Unlearning in Session-Based Recommendation

1 code implementation22 Dec 2023 Xin Xin, Liu Yang, Ziqi Zhao, Pengjie Ren, Zhumin Chen, Jun Ma, Zhaochun Ren

On the one hand, these approaches cannot achieve satisfying unlearning effects due to the collaborative correlations and sequential connections between the unlearning item and the remaining items in the session.

Session-Based Recommendations

Zero-Shot Position Debiasing for Large Language Models

no code implementations2 Jan 2024 Zhongkun Liu, Zheng Chen, Mengqi Zhang, Zhaochun Ren, Pengjie Ren, Zhumin Chen

Existing debiasing methods for LLMs require external bias knowledge or annotated non-biased samples, which is lacking for position debiasing and impractical in reality.

Position

Answer Retrieval in Legal Community Question Answering

1 code implementation9 Jan 2024 Arian Askari, Zihui Yang, Zhaochun Ren, Suzan Verberne

Furthermore, we propose LegalQA: a real-world benchmark dataset for evaluating answer retrieval in the legal domain.

Community Question Answering Retrieval

A Multi-Agent Conversational Recommender System

no code implementations2 Feb 2024 Jiabao Fang, Shen Gao, Pengjie Ren, Xiuying Chen, Suzan Verberne, Zhaochun Ren

First, we design a multi-agent act planning framework, which can control the dialogue flow based on four LLM-based agents.

Recommendation Systems

LLM-based Federated Recommendation

no code implementations15 Feb 2024 Jujia Zhao, Wenjie Wang, Chen Xu, Zhaochun Ren, See-Kiong Ng, Tat-Seng Chua

Nevertheless, applying Fed4Rec to LLM-based recommendation presents two main challenges: first, an increase in the imbalance of performance across clients, affecting the system's efficiency over time, and second, a high demand on clients' computational and storage resources for local training and inference of LLMs.

Federated Learning Language Modelling +2

KnowTuning: Knowledge-aware Fine-tuning for Large Language Models

2 code implementations17 Feb 2024 Yougang Lyu, Lingyong Yan, Shuaiqiang Wang, Haibo Shi, Dawei Yin, Pengjie Ren, Zhumin Chen, Maarten de Rijke, Zhaochun Ren

To address these problems, we propose a knowledge-aware fine-tuning (KnowTuning) method to explicitly and implicitly improve the knowledge awareness of LLMs.

Question Answering

VN Network: Embedding Newly Emerging Entities with Virtual Neighbors

no code implementations21 Feb 2024 Yongquan He, Zihan Wang, Peng Zhang, Zhaopeng Tu, Zhaochun Ren

To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.

Knowledge Graph Completion Network Embedding

How Large Language Models Encode Context Knowledge? A Layer-Wise Probing Study

1 code implementation25 Feb 2024 Tianjie Ju, Weiwei Sun, Wei Du, Xinwei Yuan, Zhaochun Ren, Gongshen Liu

Previous work has showcased the intriguing capability of large language models (LLMs) in retrieving facts and processing context knowledge.

Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning

no code implementations27 Feb 2024 Pengjie Ren, Chengshun Shi, Shiguang Wu, Mengqi Zhang, Zhaochun Ren, Maarten de Rijke, Zhumin Chen, Jiahuan Pei

Parameter-efficient fine-tuning (PEFT) is a popular method for tailoring pre-trained large language models (LLMs), especially as the models' scale and the diversity of tasks increase.

Instruction Following Natural Language Understanding

Exploiting Emotion-Semantic Correlations for Empathetic Response Generation

1 code implementation27 Feb 2024 Zhou Yang, Zhaochun Ren, Yufeng Wang, Xiaofei Zhu, Zhihao Chen, Tiecheng Cai, Yunbing Wu, Yisong Su, Sibo Ju, Xiangwen Liao

Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses.

Dialogue Generation Empathetic Response Generation +1

An Iterative Associative Memory Model for Empathetic Response Generation

no code implementations28 Feb 2024 Zhou Yang, Zhaochun Ren, Yufeng Wang, Chao Chen, Haizhou Sun, Xiaofei Zhu, Xiangwen Liao

Empathetic response generation is to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses.

Empathetic Response Generation Response Generation

Learning to Use Tools via Cooperative and Interactive Agents

no code implementations5 Mar 2024 Zhengliang Shi, Shen Gao, Xiuyi Chen, Lingyong Yan, Haibo Shi, Dawei Yin, Zhumin Chen, Pengjie Ren, Suzan Verberne, Zhaochun Ren

Tool learning empowers large language models (LLMs) as agents to use external tools to extend their capability.

Generative News Recommendation

1 code implementation6 Mar 2024 Shen Gao, Jiabao Fang, Quan Tu, Zhitao Yao, Zhumin Chen, Pengjie Ren, Zhaochun Ren

In this paper, we propose a novel generative news recommendation paradigm that includes two steps: (1) Leveraging the internal knowledge and reasoning capabilities of the Large Language Model (LLM) to perform high-level matching between candidate news and user representation; (2) Generating a coherent and logically structured narrative based on the associations between related news and user interests, thus engaging users in further reading of the news.

Language Modelling Large Language Model +1

Improving the Robustness of Large Language Models via Consistency Alignment

no code implementations21 Mar 2024 Yukun Zhao, Lingyong Yan, Weiwei Sun, Guoliang Xing, Shuaiqiang Wang, Chong Meng, Zhicong Cheng, Zhaochun Ren, Dawei Yin

The training process is accomplished by self-rewards inferred from the trained model at the first stage without referring to external human preference resources.

Instruction Following Response Generation

Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation

no code implementations25 Mar 2024 Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren

We conduct extensive experiments to evaluate the performance of representative sequential recommendation models in the setting of lifelong sequences.

Sequential Recommendation

Enhanced Generative Recommendation via Content and Collaboration Integration

no code implementations27 Mar 2024 Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, Xin Xin

However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.

Collaborative Filtering Language Modelling +1

CAUSE: Counterfactual Assessment of User Satisfaction Estimation in Task-Oriented Dialogue Systems

no code implementations27 Mar 2024 Amin Abolghasemi, Zhaochun Ren, Arian Askari, Mohammad Aliannejadi, Maarten de Rijke, Suzan Verberne

In this work, we leverage large language models (LLMs) and unlock their ability to generate satisfaction-aware counterfactual dialogues to augment the set of original dialogues of a test collection.

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