OpenRaiser/Intern-Atlas huggingface dataset:
Contextualized formula search using math abstract meaning representation
Authors: Behrooz Mansouri, Douglas W. Oard, Richard Zanibbi
Venue: Conference CIKM 2022
Year: 2022

In math formula search, relevance is determined not only by the similarity of formulas in isolation, but also by their surrounding context. We introduce MathAMR, a new unified representation for sentences containing math. MathAMR generalizes Abstract Meaning Representation (AMR) graphs to include math formula operations and arguments. We then use Sentence-BERT to embed linearized MathAMR graphs for use in formula retrieval. In our first experiment, we compare MathAMR against raw text using the same formula representation (Operator Trees), and find that MathAMR produces more effective rankings. We then apply our MathAMR embeddings to reranking runs from the ARQMath-2 formula retrieval task, where in most cases effectiveness measures are improved. The strongest reranked run matches the best P$'$@10 for an original run, and exceeds the original runs in nDCG$'$@10.
Going beyond content richness: Verified information aware summarization of crisis-related microblogs
Authors: Ashish Sharma, Koustav Rudra, Niloy Ganguly
Venue: Conference CIKM 2019
Year: 2019

High-impact catastrophic events (bomb attacks, shootings) trigger posting of large volume of information on social media platforms such as Twitter. Recent works have proposed content-aware systems for summarizing this information, thereby facilitating post-disaster services. However, a significant proportion of the posted content is unverified, which restricts the practical usage of the existing summarization systems. In this paper, we work on the novel task of generating verified summaries of information posted on Twitter during disasters. We first jointly learn representations of content-classes and expression-classes of tweets posted during disasters using a novel LDA-based generative model. These representations of content & expression classes are used in conjunction with pre-disaster user behavior and temporal signals (replies) for training a Tree-LSTM based tweet-verification model. The model infers tweet verification probabilities which are used, besides information content of tweets, in an Integer Linear Programming (ILP) framework for generating the desired verified summaries. The summaries are fine-tuned using the class information of the tweets as obtained from the LDA-based generative model. Extensive experiments are performed on a publicly-available labeled dataset of man-made disasters which demonstrate the effectiveness of our tweet-verification (3-13% gain over baselines) and summarization (12-48% gain in verified content proportion, 8-13% gain in ROUGE-score over state-of-the-art) systems. We make implementations of our various modules available online.
Word sense disambiguation by refining target word embedding
Authors: Xuefeng Zhang, Richong Zhang, Xiaoyang Li, Fanshuang Kong, Junfan Chen, Samuel Mensah, Yongyi Mao
Venue: Conference WWW 2023
Year: 2023

Word Sense Disambiguation (WSD) which aims to identify the correct sense of a target word appearing in a specific context is essential for web text analysis. The use of glosses has been explored as a means for WSD. However, only a few works model the correlation between the target context and gloss. We add to the body of literature by presenting a model that employs a multi-head attention mechanism on deep contextual features of the target word and candidate glosses to refine the target word embedding. Furthermore, to encourage the model to learn the relevant part of target features that align with the correct gloss, we recursively alternate attention on target word features and that of candidate glosses to gradually extract the relevant contextual features of the target word, refining its representation and strengthening the final disambiguation results. Empirical studies on the five most commonly used benchmark datasets show that our proposed model is effective and achieves state-of-the-art results.
RTBAgent: a LLM-based agent system for real-time bidding
Authors: Leng Cai, Junxuan He, Yikai Li, Junjie Liang, Yuanping Lin, Ziming Quan, Yawen Zeng, Jin Xu
Venue: Conference WWW 2025
Year: 2025

Real-Time Bidding (RTB) enables advertisers to place competitive bids on impression opportunities instantaneously, striving for cost-effectiveness in a highly competitive landscape. Although RTB has widely benefited from the utilization of technologies such as deep learning and reinforcement learning, the reliability of related methods often encounters challenges due to the discrepancies between online and offline environments and the rapid fluctuations of online bidding. To handle these challenges, RTBAgent is proposed as the first RTB agent system based on large language models (LLMs), which synchronizes real competitive advertising bidding environments and obtains bidding prices through an integrated decision-making process. Specifically, obtaining reasoning ability through LLMs, RTBAgent is further tailored to be more professional for RTB via involved auxiliary modules, i.e., click-through rate estimation model, expert strategy knowledge, and daily reflection. In addition, we propose a two-step decision-making process and multi-memory retrieval mechanism, which enables RTBAgent to review historical decisions and transaction records and subsequently make decisions more adaptive to market changes in real-time bidding. Empirical testing with real advertising datasets demonstrates that RTBAgent significantly enhances profitability. The RTBAgent code will be publicly accessible at: https://github.com/CaiLeng/RTBAgent.
Understanding event predictions via contextualized multilevel feature learning
Authors: Songgaojun Deng, Huzefa Rangwala, Yue Ning
Venue: Conference CIKM 2021
Year: 2021

Deep learning models have been studied to forecast human events using vast volumes of data, yet they still cannot be trusted in certain applications such as healthcare and disaster assistance due to the lack of interpretability. Providing explanations for event predictions not only helps practitioners understand the underlying mechanism of prediction behavior but also enhances the robustness of event analysis. Improving the transparency of event prediction models is challenging given the following factors: (i) multilevel features exist in event data which creates a challenge to cross-utilize different levels of data; (ii) features across different levels and time steps are heterogeneous and dependent; and (iii) static model-level interpretations cannot be easily adapted to event forecasting given the dynamic and temporal characteristics of the data. Recent interpretation methods have proven their capabilities in tasks that deal with graph-structured or relational data. In this paper, we present a Contextualized Multilevel Feature learning framework, CMF, for interpretable temporal event prediction. It consists of a predictor for forecasting events of interest and an explanation module for interpreting model predictions. We design a new context-based feature fusion method to integrate multiple levels of heterogeneous features. We also introduce a temporal explanation module to determine sequences of text and subgraphs that have crucial roles in a prediction. We conduct extensive experiments on several real-world datasets of political and epidemic events. We demonstrate that the proposed method is competitive compared with the state-of-the-art models while possessing favorable interpretation capabilities.
Context-aware event forecasting via graph disentanglement
Authors: Yunshan Ma 0002, Chenchen Ye 0001, Zijian Wu 0003, Xiang Wang 0010, Yixin Cao 0002, Tat-Seng Chua
Venue: Conference KDD 2023
Year: 2023

Event forecasting has been a demanding and challenging task throughout the entire human history. It plays a pivotal role in crisis alarming and disaster prevention in various aspects of the whole society. The task of event forecasting aims to model the relational and temporal patterns based on historical events and makes forecasting to what will happen in the future. Most existing studies on event forecasting formulate it as a problem of link prediction on temporal event graphs. However, such pure structured formulation suffers from two main limitations: 1) most events fall into general and high-level types in the event ontology, and therefore they tend to be coarse-grained and offers little utility which inevitably harms the forecasting accuracy; and 2) the events defined by a fixed ontology are unable to retain the out-of-ontology contextual information. To address these limitations, we propose a novel task of context-aware event forecasting which incorporates auxiliary contextual information. First, the categorical context provides supplementary fine-grained information to the coarse-grained events. Second and more importantly, the context provides additional information towards specific situation and condition, which is crucial or even determinant to what will happen next. However, it is challenging to properly integrate context into the event forecasting framework, considering the complex patterns in the multi-context scenario. Towards this end, we design a novel framework named Separation and Collaboration Graph Disentanglement (short as SeCoGD) for context-aware event forecasting. Since there is no available dataset for this novel task, we construct three large-scale datasets based on GDELT. Experimental results demonstrate that our model outperforms a list of SOTA methods.
Llm-generated fake news induces truth decay in news ecosystem: A case study on neural news recommendation
Authors: Beizhe Hu, Qiang Sheng, Juan Cao, Yang Li, Danding Wang
Venue: Conference SIGIR 2025
Year: 2025

Online fake news moderation now faces a new challenge brought by the malicious use of large language models (LLMs) in fake news production. Though existing works have shown LLM-generated fake news is hard to detect from an individual aspect, it remains underexplored how its large-scale release will impact the news ecosystem. In this study, we develop a simulation pipeline and a dataset with ~56k generated news of diverse types to investigate the effects of LLM-generated fake news within neural news recommendation systems. Our findings expose a truth decay phenomenon, where real news is gradually losing its advantageous position in news ranking against fake news as LLM-generated news is involved in news recommendation. We further provide an explanation about why truth decay occurs from a familiarity perspective and show the positive correlation between perplexity and news ranking. Finally, we discuss the threats of LLM-generated fake news and provide possible countermeasures. We urge stakeholders to address this emerging challenge to preserve the integrity of news ecosystems.
C3ai: Crafting and evaluating constitutions for constitutional ai
Authors: Yara Kyrychenko, Ke Zhou, Edyta Paulina Bogucka, Daniele Quercia
Venue: Conference WWW 2025
Year: 2025

Constitutional AI (CAI) guides LLM behavior using constitutions, but identifying which principles are most effective for model alignment remains an open challenge. We introduce the C3AI framework (\textit{Crafting Constitutions for CAI models}), which serves two key functions: (1) selecting and structuring principles to form effective constitutions before fine-tuning; and (2) evaluating whether fine-tuned CAI models follow these principles in practice. By analyzing principles from AI and psychology, we found that positively framed, behavior-based principles align more closely with human preferences than negatively framed or trait-based principles. In a safety alignment use case, we applied a graph-based principle selection method to refine an existing CAI constitution, improving safety measures while maintaining strong general reasoning capabilities. Interestingly, fine-tuned CAI models performed well on negatively framed principles but struggled with positively framed ones, in contrast to our human alignment results. This highlights a potential gap between principle design and model adherence. Overall, C3AI provides a structured and scalable approach to both crafting and evaluating CAI constitutions.
Uncovering the heterogeneous effects of preference diversity on user activeness: A dynamic mixture model
Authors: Yunfei Lu, Peng Cui 0001, Linyun Yu, Lei Li 0005, Wenwu Zhu 0001
Venue: Conference KDD 2022
Year: 2022

Preference diversity arouses much research attention in recent years, as it is believed to be closely related to many profound problems such as user activeness in social media or recommendation systems. However, due to the lack of large-scale data with comprehensive user behavior log and accurate content labels, the real quantitative effect of preference diversity on user activeness is still largely unknown. This paper studies the heterogeneous effect of preference diversity on user activeness in social media. We examine large-scale real-world datasets collected from two of the most popular video-sharing social platforms in China, including the behavior logs of more than 787 thousand users and 1.95 million videos, with accurate content category information. We investigate the distribution and evolution of preference diversity, and find rich heterogeneity in the effect of preference diversity on the dynamic activeness. Furthermore, we discover the divergence of preference diversity mechanisms for the same user under different usage scenarios, such as active (where users actively seek information) and passive (where users passively receive information) modes. Unlike existing qualitative studies, we propose a universal mixture model with the capability of accurately fitting dynamic activeness curves while reflecting the heterogeneous patterns of preference diversity. To our best knowledge, this is the first quantitative model that incorporates the effect of preference diversity on user activeness. With the modeling parameters, we are able to make accurate churn and activeness predictions and provide decision support for increasing user activity through the intervention of diversity. Our findings and model comprehensively reveal the significance of preference diversity and provide potential implications for the design of future recommendation systems and social media.
Part: Enhancing proactive social chatbots with personalized real-time retrieval
Authors: Zihan Niu, Zheyong Xie, Shaosheng Cao, Chonggang Lu, Zheyu Ye, Tong Xu, Zuozhu Liu, Yan Gao, Jia Chen, Zhe Xu, Yi Wu, Yao Hu
Venue: Conference SIGIR 2025
Year: 2025

Social chatbots have become essential intelligent companions in daily scenarios ranging from emotional support to personal interaction. However, conventional chatbots with passive response mechanisms usually rely on users to initiate or sustain dialogues by bringing up new topics, resulting in diminished engagement and shortened dialogue duration. In this paper, we present PaRT, a novel framework enabling context-aware proactive dialogues for social chatbots through personalized real-time retrieval and generation. Specifically, PaRT first integrates user profiles and dialogue context into a large language model (LLM), which is initially prompted to refine user queries and recognize their underlying intents for the upcoming conversation. Guided by refined intents, the LLM generates personalized dialogue topics, which then serve as targeted queries to retrieve relevant passages from RedNote. Finally, we prompt LLMs with summarized passages to generate knowledge-grounded and engagement-optimized responses. Our approach has been running stably in a real-world production environment for more than 30 days, achieving a 21.77\% improvement in the average duration of dialogues.
Ucepic: Unifying aspect planning and lexical constraints for generating explanations in recommendation
Authors: Jiacheng Li 0003, Zhankui He, Jingbo Shang, Julian J. McAuley
Venue: Conference KDD 2023
Year: 2023

Personalized natural language generation for explainable recommendations plays a key role in justifying why a recommendation might match a user's interests. Existing models usually control the generation process by aspect planning. While promising, these aspect-planning methods struggle to generate specific information correctly, which prevents generated explanations from being convincing. In this paper, we claim that introducing lexical constraints can alleviate the above issues. We propose a model, UCEpic, that generates high-quality personalized explanations for recommendation results by unifying aspect planning and lexical constraints in an insertion-based generation manner. Methodologically, to ensure text generation quality and robustness to various lexical constraints, we pre-train a non-personalized text generator via our proposed robust insertion process. Then, to obtain personalized explanations under this framework of insertion-based generation, we design a method of incorporating aspect planning and personalized references into the insertion process. Hence, UCEpic unifies aspect planning and lexical constraints into one framework and generates explanations for recommendations under different settings. Compared to previous recommendation explanation generators controlled by only aspects, UCEpic incorporates specific information from keyphrases and then largely improves the diversity and informativeness of generated explanations for recommendations on datasets such as RateBeer and Yelp.
Multimodal conditional retrieval with high controllability
Authors: Xiaoda Yang, Xize Cheng, Minghui Fang, Hongshun Qiu, Yuhang Ma, Junyu Lu, Jiaqi Duan, Sihang Cai, Zehan Wang, Ruofan Hu, Dongjie Fu, Zhou Zhao, Tao Jin
Venue: Conference KDD 2025
Year: 2025

Searching for images using text has limitations because language has difficulties in expressing certain abstract intentions, e.g. artistic styles are difficult to describe for non-experts. As for the image search image model, images can convey abstract intentions, but cannot express the specific purpose, so many of the current graph search works only have a single function, such as content search and style search. Our work aims to combine the strengths of both, merging the ability of text to express specific ideas with the ability of images to convey abstract concepts, thus achieving a better capture of the user's intentions. To this end, we propose CCSR, a multimodal conditional content-style joint retrieval model. Our model is the first to apply contrastive learning to conditional retrieval and introduces a novel Mixture-of-Expert models (MOE) system to enable collaboration between multiple expert systems. We adopt a novel prompt learning strategy that allows the model to adaptively select specific prompts, thereby enhancing its focus on the current task. In addition, to evaluate the joint content-style retrieval capability of our model, we present a new dataset, StyleCoco, containing rich content categories and style categories. The experimental results indicate that CCSR has achieved state-of-the-art performance in conditional style retrieval, content retrieval, and style-content retrieval. The dataset and code will be publicly available on https://mccsr.github.io/.
The elusiveness of detecting political bias in language models
Authors: Riccardo Lunardi, David La Barbera, Kevin Roitero
Venue: Conference CIKM 2024
Year: 2024

This study challenges the prevailing approach of measuring political leanings in Large Language Models (LLMs) through direct questioning. By extensively testing LLMs with original, positively and negatively paraphrased Political Compass questions we demonstrate that LLMs do not consistently reveal their political biases in response to standard questions. Our findings indicate that LLMs' political orientations are elusive, easily influenced by subtle changes in phrasing and context. This study underscores the limitations of direct questioning in accurately measuring the political biases of LLMs and emphasizes the necessity for more refined and effective approaches to understand their true political stances.
Can pre-trained language models understand chinese humor?
Authors: Yuyan Chen, Zhixu Li, Jiaqing Liang, Yanghua Xiao, Bang Liu, Yunwen Chen
Venue: Conference WSDM 2023
Year: 2023

Humor understanding is an important and challenging research in natural language processing. As the popularity of pre-trained language models (PLMs), some recent work makes preliminary attempts to adopt PLMs for humor recognition and generation. However, these simple attempts do not substantially answer the question: whether PLMs are capable of humor understanding? This paper is the first work that systematically investigates the humor understanding ability of PLMs. For this purpose, a comprehensive framework with three evaluation steps and four evaluation tasks is designed. We also construct a comprehensive Chinese humor dataset, which can fully meet all the data requirements of the proposed evaluation framework. Our empirical study on the Chinese humor dataset yields some valuable observations, which are of great guiding value for future optimization of PLMs in humor understanding and generation.
A GAIL fine-tuned LLM enhanced framework for low-resource knowledge graph question answering
Authors: Zhiqiang Zhang, Liqiang Wen, Wen Zhao
Venue: Conference CIKM 2024
Year: 2024

Recent studies on knowledge graph question answering (KGQA) have focused on tackling complex inquiries to enhance the applicability of models in real-life settings. Unfortunately, KGQA models encounter significant challenges due to the lack of high-quality annotated data, making it difficult to accurately answer the diverse range of complex natural language questions posed by users. Inspired by the recent success of Large Language Models (LLMs), the burden associated with manual annotation can be mitigated by utilizing LLMs. However, the data generated directly by LLMs may exhibit a potential distribution discrepancy with real user queries. In this paper, we present an enhancement framework that utilizes Generative Adversarial Imitation Learning (GAIL) to fine-tune LLMs, which can address the challenges inherent in the low-resource KGQA task. Specifically, based on GAIL, the LLMs act as the generator aiming to output samples resembling expert demonstrations. Meanwhile, we utilize a paired discriminator to assess the authenticity of generated sequences and their relevance to the input SPARQL queries. Additionally, proximal policy optimization is leveraged to stabilize the training of the generator. Furthermore, we employ an automated algorithm to controllably sample various SPARQL queries from the knowledge graph, subsequently transforming them into corresponding natural language questions using fine-tuned LLMs. The synthetic dataset can serve as supplementary data for training lightweight KGQA models in real-world scenarios. Experimental results on the WebQuestionsSP, ComplexWebQuestions, and GrailQA show that our framework achieves state-of-the-art performance in a low-resource setting, even approaching the performance of supervised models.