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Conference on Empirical methods in Natural Language Processing (EMNLP)

作者： Chen, Zhipeng Zhou, Kun Zhang, Beichen Gong, Zheng Zhao, Wayne Xin Wen, Ji-Rong Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Renmin Univ China Sch Informat Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9798891760615

Although large language models (LLMs) have achieved excellent performance in a variety of evaluation benchmarks, they still struggle in complex reasoning tasks which require specific knowledge and multi-hop reasoning. To improve the reasoning abilities, we propose ChatCoT, a tool-augmented chain-of-thought reasoning framework for chat-based LLMs (e.g., ChatGPT). In ChatCoT, we model the chain-of-thought (CoT) reasoning as multi-turn conversations, to utilize tools in a more natural way through chatting. At each turn, LLMs can either interact with tools or perform the reasoning. Our approach can effectively leverage the multi-turn conversation ability of chat-based LLMs, and integrate the thought chain following and tools manipulation in a unified way. Specially, we initialize the early turns of the conversation by the knowledge about tools, tasks, and reasoning format, and propose an iterative tool-augmented reasoning step to perform step-by-step tool-augmented reasoning. The experiment results on two complex reasoning datasets (MATH and HotpotQA) have shown the effectiveness of ChatCoT on complex reasoning tasks, achieving a 7.9% relative improvement over the state-of-the-art baseline. Our code and data are available at: https://***/RUCAIBOX/ChatCoT.

关键词： Benchmarking

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Persuading across Diverse Domains: A dataset and Persuasion Large Language Model 62

Persuading across Diverse Domains: A Dataset and Persuasion ...

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62nd Annual Meeting of the Association-for-Computational-Linguistics (ACL) / Student Research Workshop (SRW)

作者： Jin, Chuhao Ren, Kening Kong, Lingzhen Wang, Xiting Song, Ruihua Chen, Huan Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Meituan Beijing Peoples R China

ISBN: (纸本)9798891760943

Persuasive dialogue requires multi-turn following and planning abilities to achieve the goal of persuading users, which is still challenging even for state-of-the-art large language models (LLMs). Previous works focus on retrieval-based models or generative models in a specific domain due to a lack of data across multiple domains. In this paper, we leverage GPT-4 to create the first multi-domain persuasive dialogue dataset DailyPersuasion. Then we propose a general method named PersuGPT to learn a persuasion model based on LLMs through intent-to-strategy reasoning, which summarizes the intent of user's utterance and reasons next strategy to respond. Moreover, we design a simulation-based preference optimization, which utilizes a learned user model and our model to simulate next turns and estimate their rewards more accurately. Experimental results on two datasets indicate that our proposed method outperforms all baselines in terms of automatic evaluation metric Win-Rate and human evaluation. The code and data are available at https://***.

关键词： Computational linguistics

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Improving Sequential Recommendation with Knowledge-Enhanced Memory Networks 18

Improving Sequential Recommendation with Knowledge-Enhanced ...

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41st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Huang, Jin Zhao, Wayne Xin Dou, Hongjian Wen, Ji-Rong Chang, Edward Y. Renmin Univ China Sch Informat Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China HTC Res & Innovat New Taipei Taiwan

ISBN: (纸本)9781450356572

With the revival of neural networks, many studies try to adapt powerful sequential neural models, i.e., Recurrent Neural Networks (RNN), to sequential recommendation. RNN-based networks encode historical interaction records into a hidden state vector. Although the state vector is able to encode sequential dependency, it still has limited representation power in capturing complicated user preference. It is difficult to capture fine-grained user preference from the interaction sequence. Furthermore, the latent vector representation is usually hard to understand and explain. To address these issues, in this paper, we propose a novel knowledge enhanced sequential recommender. Our model integrates the RNN-based networks with key-Value Memory Network (KV-MN). We further incorporate knowledge base (KB) information to enhance the semantic representation of KV-MN. RNN-based models are good at capturing sequential user preference, while knowledge-enhanced KV-MNs are good at capturing attribute-level user preference. By using a hybrid of RNNs and KV-MNs, it is expected to be endowed with both benefits from these two components. The sequential preference representation together with the attribute-level preference representation are combined as the final representation of user preference. With the incorporation of KB information, our model is also highly interpretable. To our knowledge, it is the first time that sequential recommender is integrated with external memories by leveraging large-scale KB information.

关键词： Sequential recommendation memory network knowledge base

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Automatically Mining Facets for Queries from Their Search Results

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2016年第2期28卷 385-397页

作者： Dou, Zhicheng Jiang, Zhengbao Hu, Sha Wen, Ji-Rong Song, Ruihua Renmin Univ China Beijing Key Lab Big Data Management & Anal Method Sch Informat Beijing 100872 Peoples R China Renmin Univ China DEKE Beijing 100872 Peoples R China Microsoft Res Beijing Peoples R China

We address the problem of finding query facets which are multiple groups of words or phrases that explain and summarize the content covered by a query. We assume that the important aspects of a query are usually presented and repeated in the query's top retrieved documents in the style of lists, and query facets can be mined out by aggregating these significant lists. We propose a systematic solution, which we refer to as QDMiner, to automatically mine query facets by extracting and grouping frequent lists from free text, HTML tags, and repeat regions within top search results. Experimental results show that a large number of lists do exist and useful query facets can be mined by QDMiner. We further analyze the problem of list duplication, and find better query facets can be mined by modeling fine-grained similarities between lists and penalizing the duplicated lists.

关键词： Query facet faceted search summarization user intent

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Personalizing Search Results Using Hierarchical RNN with Query-aware Attention 18

Personalizing Search Results Using Hierarchical RNN with Que...

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27th ACM International Conference on Information and Knowledge management (CIKM)

作者： Ge, Songwei Dou, Zhicheng Jiang, Zhengbao Nie, Jian-Yun Wen, Ji-Rong Renmin Univ China Sch Informat Beijing Peoples R China Univ Montreal DIRO Montreal PQ Canada Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China Beijing Inst Technol Natl Engn Lab Big Data Syst Software Beijing Peoples R China MOE Key Lab Data Engn & Knowledge Engn Beijing Peoples R China

ISBN: (纸本)9781450360142

Search results personalization has become an effective way to improve the quality of search engines. Previous studies extracted information such as past clicks, user topical interests, query click entropy and so on to tailor the original ranking. However, few studies have taken into account the sequential information underlying previous queries and sessions. Intuitively, the order of issued queries is important in inferring the real user interests. And more recent sessions should provide more reliable personal signals than older sessions. In addition, the previous search history and user behaviors should influence the personalization of the current query depending on their relatedness. To implement these intuitions, in this paper we employ a hierarchical recurrent neural network to exploit such sequential information and automatically generate user profile from historical data. We propose a query-aware attention model to generate a dynamic user profile based on the input query. Significant improvement is observed in the experiment with data from a commercial search engine when compared with several traditional personalization models. Our analysis reveals that the attention model is able to attribute higher weights to more related past sessions after fine training.

关键词： search results personalization hierarchical recurrent neural network query-aware attention

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EulerFormer: Sequential User Behavior Modeling with Complex Vector Attention 47

EulerFormer: Sequential User Behavior Modeling with Complex ...

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47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Tian, Zhen Zhao, Wayne Xin Zhang, Changwang Zhao, Xin Ma, Zhongrui Wen, Ji-Rong Renmin Univ China GSAI Beijing Peoples R China Huawei Poss Lab Beijing Peoples R China Gaoling Sch Artificial Intelligence Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9798400704314

To capture user preference, transformer models have been widely applied to model sequential user behavior data. The core of transformer architecture lies in the self-attention mechanism, which computes the pairwise attention scores in a sequence. Due to the permutation-equivariant nature, positional encoding is used to enhance the attention between token representations. In this setting, the pairwise attention scores can be derived by both semantic difference and positional difference. However, prior studies often model the two kinds of difference measurements in different ways, which potentially limits the expressive capacity of sequence modeling. To address this issue, this paper proposes a novel transformer variant with complex vector attention, named EulerFormer, which provides a unified theoretical framework to formulate both semantic difference and positional difference. The EulerFormer involves two key technical improvements. First, it employs a new transformation function for efficiently transforming the sequence tokens into polar-form complex vectors using Euler's formula, enabling the unified modeling of both semantic and positional information in a complex rotation form. Secondly, it develops a differential rotation mechanism, where the semantic rotation angles can be controlled by an adaptation function, enabling the adaptive integration of the semantic and positional information according to the semantic contexts. Furthermore, a phase contrastive learning task is proposed to improve the isotropy of contextual representations in EulerFormer. Our theoretical framework possesses a high degree of completeness and generality (e.g., RoPE can be instantiated as a special case). It is more robust to semantic variations and possesses more superior theoretical properties (e.g., long-term decay) in principle. Extensive experiments conducted on four public datasets demonstrate the effectiveness and efficiency of our approach. Our code is available at https://github

关键词： Complex Vector Attention User Behavior Modeling

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Generating Query Facets Using Knowledge Bases

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2017年第2期29卷 315-329页

作者： Jiang, Zhengbao Dou, Zhicheng Wen, Ji-Rong Renmin Univ China Sch Informat Beijing 100872 Peoples R China Renmin Univ China DEKE Beijing 100872 Peoples R China Renmin Univ China Sch Informat Beijing 100872 Peoples R China Renmin Univ China Beijing Key Lab Big Data Management & Anal Method Beijing 100872 Peoples R China

A query facet is a significant list of information nuggets that explains an underlying aspect of a query. Existing algorithms mine facets of a query by extracting frequent lists contained in top search results. The coverage of facets and facet items mined by these kind of methods might be limited, because only a small number of search results are used. In order to solve this problem, we propose mining query facets by using knowledge bases which contain high-quality structured data. Specifically, we first generate facets based on the properties of the entities which are contained in Freebase and correspond to the query. Second, we mine initial query facets from search results, then expanding them by finding similar entities from Freebase. Experimental results show that our proposed method can significantly improve the coverage of facet items over the state-of-the-art algorithms.

关键词： Query facets knowledge bases query dimensions

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Estimating α-Rank from A Few Entries with Low Rank Matrix Completion 38

Estimating α-Rank from A Few Entries with Low Rank Matrix C...

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International Conference on Machine Learning (ICML)

作者： Du, Yali Yan, Xue Chen, Xu Wang, Jun Zhang, Haifeng UCL London England Chinese Acad Sci Inst Automat Beijing Peoples R China Renmin Univ China Beijing Key Lab Big Data Management & Anal Method GSAI Beijing Peoples R China

ISBN: (纸本)9781713845065

Multi-agent evaluation aims at the assessment of an agent's strategy on the basis of interaction with others. Typically, existing methods such as alpha-rank and its approximation still require to exhaustively compare all pairs of joint strategies for an accurate ranking, which in practice is computationally expensive. In this paper, we aim to reduce the number of pairwise comparisons in recovering a satisfying ranking for n strategies in two-player meta-games, by exploring the fact that agents with similar skills may achieve similar payoffs against others. Two situations are considered: the first one is when we can obtain the true payoffs;the other one is when we can only access noisy payoff. Based on these formulations, we leverage low-rank matrix completion and design two novel algorithms for noise-free and noisy evaluations respectively. For both of these settings, we theorize that O(nr log n) (n is the number of agents and r is the rank of the payoff matrix) payoff entries are required to achieve sufficiently well strategy evaluation performance. Empirical results on evaluating the strategies in three synthetic games and twelve real world games demonstrate that strategy evaluation from a few entries can lead to comparable performance to algorithms with full knowledge of the payoff matrix.

关键词： Petroleum reservoir evaluation

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Multi-Modal Experience Inspired AI Creation 22

Multi-Modal Experience Inspired AI Creation

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30th ACM International Conference on Multimedia (MM)

作者： Cao, Qian Chen, Xu Song, Ruihua Jiang, Hao Yang, Guang Cao, Zhao Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Huawei Poisson Lab Shenzhen Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China

ISBN: (纸本)9781450392037

AI creation, such as poem or lyrics generation, has attracted increasing attention from both industry and academic communities, with many promising models proposed in the past few years. Existing methods usually estimate the outputs based on single and independent visual or textual information. However, in reality, humans usually make creations according to their experiences, which may involve different modalities and be sequentially correlated. To model such human capabilities, in this paper, we define and solve a novel AI creation problem based on human experiences. More specifically, we study how to generate texts based on sequential multi-modal information. Compared with the previous works, this task is much more difficult because the designed model has to well understand and adapt the semantics among different modalities and effectively convert them into the output in a sequential manner. To alleviate these difficulties, we firstly design a multi-channel sequence-to-sequence architecture equipped with a multi-modal attention network. For more effective optimization, we then propose a curriculum negative sampling strategy tailored for the sequential inputs. To benchmark this problem and demonstrate the effectiveness of our model, we manually labeled a new multi-modal experience dataset. With this dataset, we conduct extensive experiments by comparing our model with a series of representative baselines, where we can demonstrate significant improvements in our model based on both automatic and human-centered metrics. The code and data are available at: https://***/Aman-4-Real/MMTG.

关键词： AI Creation Experience Multi-modal

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Adapting User Preference to Online Feedback in Multi-round Conversational Recommendation 21

Adapting User Preference to Online Feedback in Multi-round C...

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14th ACM International Conference on Web Search and data Mining (WSDM)

作者： Xu, Kerui Yang, Jingxuan Xu, Jun Gao, Sheng Guo, Jun Wen, Ji-Rong Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Renmin Univ China Gaoling Sch Artificial Intelligence Beijing Peoples R China Beijing Key Lab Big Data Management & Anal Method Beijing Peoples R China GuiZhou Univ Guizhou Prov Key Lab Publ Big Data Guiyang Peoples R China

ISBN: (纸本)9781450382977

This paper concerns user preference estimation in multi-round conversational recommender systems (CRS), which interacts with users by asking questions about attributes and recommending items multiple times in one conversation. Multi-round CRS such as EAR [14] have been proposed in which the user's online feedback at both attribute level and item level can be utilized to estimate user preference and make recommendations. Though preliminary success has been shown, existing user preference models in CRS usually use the online feedback information as independent features or training instances, overlooking the relation between attribute-level and item-level feedback signals. The relation can be used to more precisely identify the reasons (e.g., some certain attributes) that trigger the rejection of an item, leading to more fine-grained utilization of the feedback information. To address aforementioned issue, this paper proposes a novel preference estimation model tailored for multi-round CRS, called Feedback-guided Preference Adaptation Network (FPAN). In FPAN, two gating modules are designed to respectively adapt the original user embedding and item-level feedback, both according to the online attribute-level feedback. The gating modules utilize the fine-grained attribute-level feedback to revise the user embedding and coarse-grained item-level feedback, achieving more accurate user preference estimation by considering the relation between feedback. Experimental results on two benchmarks showed that FPAN outperformed the state-of-the-art user preference models in CRS, and the multi-round CRS can also be enhanced by using FPAN as its recommender component.

关键词： Multi-round conversational recommendation User preference

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