OpenRaiser/Intern-Atlas huggingface dataset:
LINKin-PARK: Land Valuation Information and Knowledge in Predictive Analysis and Reporting Kit via Dual Attention-DCCNN
Authors: Teng-Yuan Tsou, Shih-Yu Lai, Hsuan-Ching Chen, Jung-Tsang Yeh, Pei-Xuan Li, Tzu-Chang Lee, Hsun-Ping Hsieh
Venue: CIKM
Year: 2024

We present LINKin-PARK, an innovative system that seamlessly merges geographic visualization with an advanced Dual Attention Double Channel Convolutional Neural Network with Multilayer Perceptron (Dual Attention-DCCNN+MLP) to facilitate the efficient analysis of land valuation. LINKin-PARK provides robust visualization capabilities for intuitive comprehension. Our model outperforms traditional methods, e.g., linear regression, multilayer perceptron (MLP), Extreme Gradient Boosting (XGBoost), and the combination of CNN (Convolutional Neural Network) with MLP. An ablation study further evaluates the influence of specific components within the model, revealing that spatial and channel-wise attention mechanisms and the integration of DCCNN and skip connections are crucial for capturing spatial details and improving prediction accuracy. Users have the flexibility to explore and predict developable land valuation based on their specific requirements and provide their feedback to minimize errors in model prediction. For instance, this system can forecast future development potential and market demand for everywhere in an urban space, enabling users to make informed decisions before purchasing a property. Similarly, retailers can anticipate future revenues to aid in strategic decisions, such as selecting optimal locations for establishing new retail outlets. In summary, LINKin-PARK effectively combines geographic visualization and Dual Attention-DCCNN+MLP to assist users in analyzing and predicting land valuation and other scenarios.
"Reasoning before Responding": Towards Legal Long-form Question Answering with Interpretability
Authors: Utkarsh Ujwal, Sai Sri Harsha Surampudi, Sayantan Mitra, Tulika Saha
Venue: CIKM
Year: 2024

Long-Form Question Answering (LFQA) represents a growing interest in Legal Natural Language Processing (Legal-NLP) as many individuals encounter legal disputes at some point in their lives, but lack of knowledge about how to negotiate these complex situations might put them at risk. The endeavor to generate detailed answers to contextually rich legal questions has faced challenges, primarily due to the limited availability of specialized datasets involving intensive manual effort or incapability of existing LFQA models to produce informative responses. Addressing this, our research introduces a semi-synthetic dataset, Legal-LFQA (L2FQA) created by exploiting a large language model (LLM) and utilizing contexts derived from existing legal datasets. Additionally, we hypothesize that integrating legal reasoning into the answer generation process of the LLMs will help bolster both the quality and interpretability of the produced responses. We systematically analyze the quality of L2FQA using human evaluation and natural language inference based metrics. Next, we benchmark L2FQA on a wide range of general-purpose and domain-specific LLMs using fine-tuning and in-context learning (with zero, one and few shot) strategies. The efficacy of these techniques is gauged through several automated and human evaluations. Results indicate that incorporating legal reasoning into the answer generation process provides an avenue for improving the quality of responses in the context of Legal-LFQA task. By addressing the challenges faced in LFQA and emphasizing the potential of interpretability, this research contributes to the foundational work in enhancing question-answering systems within the legal domain.
Knowledge Graphs for Responsible AI
Authors: Edlira Vakaj, Nandana Mihindukulasooriya, Manas Gaur, Arijit Khan
Venue: CIKM
Year: 2024

Responsible AI is built upon a set of principles that prioritize fairness, transparency, accountability, and inclusivity in AI development and deployment. As AI systems become increasingly sophisticated, including the explosion of generative AI, there is a growing need to address ethical considerations and potential societal impacts of their uses. Knowledge graphs (KGs), as structured representations of information, can enhance generative AI performance by providing context, explaining outputs, and reducing biases, thereby offering a powerful framework to address the challenges of responsible AI. By leveraging semantic relationships and contextual understanding, KGs facilitate transparent decision-making, enabling stakeholders to trace and interpret the reasoning behind AI driven outcomes. Moreover, they provide a means to capture and manage diverse knowledge sources, supporting the development of fair and unbiased AI models. The workshop aims to investigate the role of knowledge graphs in promoting responsible AI principles and creating a cooperative space for researchers, practitioners, and policymakers to exchange insights and enhance their comprehension of KGs' impact on achieving responsible AI solutions. It seeks to facilitate collaboration and idea-sharing to advance the understanding of how KGs can contribute to responsible AI.
Is the Search Engine of the Future a Chatbot?
Authors: Suzan Verberne
Venue: CIKM
Year: 2024

The rise of Large Language Models (LLMs) has had a huge impact on the interaction of users with information. Many people argue that the age of search engines as we know them has ended, while other people argue that retrieval technology is more relevant than ever before, because we need information to be grounded in sources. In my talk I will argue that both statements are true. I will discuss the multiple relations between LLMs and Information Retrieval: how can they strengthen each other, what are the challenges we face, and what directions should we go in our research?
Advertiser Content Understanding via LLMs for Google Ads Safety
Authors: Joseph Wallace, Tushar Dogra, Wei Qiao, Yuan Wang
Venue: CIKM
Year: 2024

Ads Content Safety at Google requires classifying billions of ads for Google Ads content policies. Consistent and accurate policy enforcement is important for advertiser experience and user safety and it is a challenging problem, so there is a lot of value for improving it for advertisers and users. Inconsistent policy enforcement causes increased policy friction and poor experience with good advertisers, and bad advertisers exploit the inconsistency by creating multiple similar ads in the hope that some will get through our defenses. This study proposes a method to understand advertiser's intent for content policy violations, using Large Language Models (LLMs). We focus on identifying good advertisers to reduce content over-flagging and improve advertiser experience, though the approach can easily be extended to classify bad advertisers too. We generate advertiser's content profile based on multiple signals from their ads, domains, targeting info, etc. We then use LLMs to classify the advertiser content profile, along with relying on any knowledge the LLM has of the advertiser, their products or brand, to understand whether they are likely to violate a certain policy or not. After minimal prompt tuning our method was able to reach 95% accuracy on a small test set.
Causal Probing for Dual Encoders
Authors: Jonas Wallat, Hauke Hinrichs, Avishek Anand
Venue: CIKM
Year: 2024

Dual encoders are highly effective and widely deployed in the retrieval phase for passage and document ranking, question answering, or retrieval-augmented generation (RAG) setups. Most dual-encoder models use transformer models like BERT to map input queries and output targets to a common vector space encoding the semantic similarity. Despite their prevalence and impressive performance, little is known about the inner workings of dense encoders for retrieval. We investigate neural retrievers using the probing paradigm to identify well-understood IR properties that causally result in ranking performance. Unlike existing works that have probed cross-encoders to show query-document interactions, we provide a principled approach to probe dual-encoders. Importantly, we employ causal probing to avoid correlation effects that might be artefacts of vanilla probing. We conduct extensive experiments on one such dual encoder (TCT-ColBERT) to check for the existence and relevance of six properties: term importance, lexical matching (BM25), semantic matching, question classification, and the two linguistic properties of named entity recognition and coreference resolution. Our layer-wise analysis shows important differences between re-rankers and dual encoders, establishing which tasks are not only understood by the model but also used for inference.
Behavior-aware Sparse Trajectory Recovery in Last-mile Delivery with Multi-scale Attention Fusion
Authors: Hai Wang, Shuai Wang, Li Lin, Yu Yang, Shuai Wang, Hongkai Wen
Venue: CIKM
Year: 2024

Trajectory data is a valuable asset for service management and spatio-temporal mining in transportation and logistics systems. However, due to equipment failure, network delay, and energy constraints, some trajectory point may be missed, which makes it difficult for trajectory-based management. Some researchers have focused on recovering sparse trajectories from road networks and historical trajectory data, but these methods are ineffective when the road network is incomplete. Recent research works have explored learning-based methods to recover trajectories in free space but lack user movement behavior modeling and efficient feature extraction on sparse long-range trajectories. Our work exploits the periodic behavior of couriers and fine-grained Area of Interest (AOI) data for sparse trajectory recovery in last-mile delivery. However, we face challenges with AOI access sequence deviations due to GPS inaccuracies and abnormal courier behaviors, as well as the complex, dynamic relationships within and between courier routes due to uncertain pick-up demands. To address these challenges, we design a graph-based multi-task learning framework, focusing on multi-scale attention fusion for end-to-end free space trajectory recovery. Our approach starts with a behavior-aware graph network that generates detailed spatial features. Following this, we propose a multi-scale attention fusion mechanism to extract intra- and inter-trajectory features. Finally, we design a multi-task learning module that predicts both coarse-grained spatial access sequences and fine-grained trajectory points. We evaluate the model with six-month data involved with more than 360,000 trajectory segments and more than 7.2 million waybills collected from one of the largest logistic companies in China. Extensive experiments on real-world datasets demonstrate that our method outperforms state-of-the-arts in multiple metrics.
DP-FedFace: Privacy-Preserving Facial Recognition in Real Federated Scenarios
Authors: Wenjing Wang, Si Li
Venue: CIKM
Year: 2024

Advanced deep learning-based face recognition models require extensive datasets for optimal performance. However, increasing privacy concerns drive the limitation of face image access on devices to prevent personal information leaks. To address this, federated learning, which allows decentralized data collaboration, has gained popularity. However, traditional federated learning methods risk privacy by transmitting identity proxies to servers. We propose DP-FedFace, a privacy framework specifically designed for a realistic scenario where each client contains only the owner's face images (one identity per client). It uses the difference between human and model perception to eliminate visualization-critical low-frequency components, thus protecting user privacy. We also introduce a novel, learnable privacy cost allocation mechanism that optimizes allocation strategies and adds noise to frequency domain features. Extensive experiments demonstrate that DP-FedFace maintains high recognition accuracy while offers robust privacy protection.
Learning to Differentiate Pairwise-Argument Representations for Implicit Discourse Relation Recognition
Authors: Zhipang Wang, Yu Hong, Yuxiang Lu, Xiabing Zhou, Jianmin Yao, Guodong Zhou
Venue: CIKM
Year: 2024

Recognizing implicit discourse relations between texts is challenging due to the absence of explicit connectives. Encoding texts into distinguishable semantic representations is beneficial to connective-free relation determination. To enable encoders to produce clearly distinguishable representations, we propose a joint learning framework. It combines prototypical and adversarial learning as well as hub-migration based redistribution. We experiment on PDTB 2.0, PDTB 3.0 and the CoNLL-2016 shared benchmark dataset. Experimental results show that our methods yield substantial improvements, compared to BERT, RoBERTa and DeBERTa baselines. In the separate comparison experiment where implicit connectives are disable during training, our models outperform the previous work for the four main discourse relation types (Temporality, Comparison, Contingency and Expansion), achieving F1-scores of about 60.75%, 63.48%, 75.70% and 77.09%. On the other hand, our models obtain comparable performance compared to part of the state-of-the-art models which adopt implicit connectives as observable hints for training. When the pretrained language models are used as the backbones, our methods yield the extra time consumption, which is about 1 hour at worst when training is conducted on Tesla 40GB A100 GPU.
The 'Path' to Clarity: Identifying False Claims Through a Knowledge Graph Exploration
Authors: Wenbo Wang
Venue: CIKM
Year: 2024

Automated fact-checking has emerged as a safeguard against the spread of false information. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent accuracy of veracity prediction. However, the current state-of-the-art models still face challenges, such as ambiguity in the claims and lack of contextual information. This study introduces a fact-checking model, Path-FC, which focuses on 1) augmenting the representations of claims and evidence by incorporating additional context using the Knowledge Paths extracted from the external Knowledge Graph; 2) Identifying false claims by learning the differences between claims and evidence. The experimental results demonstrate that Knowledge Path retrieval, combined with the multi-head attention technique, contributes to improved performance of fact-checking. The code is available at https://anonymous.4open.science/r/Path-FC.
Enhancing Click-through Rate Prediction in Recommendation Domain with Search Query Representation
Authors: Yuening Wang, Man Chen, Yaochen Hu, Wei Guo, Yingxue Zhang, Huifeng Guo, Yong Liu, Mark Coates
Venue: CIKM
Year: 2024

Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to search for items before providing recommendations. Since users and items are often shared between the search and recommendation domains, there is a valuable opportunity to enhance the recommendation domain by leveraging user preferences extracted from the search domain. Existing approaches either overlook the shift in user intention between these domains or fail to capture the significant impact of learning from users' search queries on understanding their interests. In this paper, we propose a framework that learns from user search query embeddings within the context of user preferences in the recommendation domain. Specifically, user search query sequences from the search domain are used to predict the items users will click at the next time point in the recommendation domain. Additionally, the relationship between queries and items is explored through contrastive learning. To address issues of data sparsity, the diffusion model is incorporated to infer positive items the user will select after searching with certain queries in a denoising manner, which is particularly effective in preventing false positives. Effectively extracting this information, the queries are integrated into click-through rate prediction in the recommendation domain. Experimental analysis demonstrates that our model outperforms state-of-the-art models in the recommendation domain.
On Causally Disentangled State Representation Learning for Reinforcement Learning based Recommender Systems
Authors: Siyu Wang, Xiaocong Chen, Lina Yao
Venue: CIKM
Year: 2024

In Reinforcement Learning-based Recommender Systems (RLRS), the complexity and dynamism of user interactions often result in high-dimensional and noisy state spaces, making it challenging to discern which aspects of the state are truly influential in driving the decision-making process. This issue is exacerbated by the evolving nature of user preferences and behaviors, requiring the recommender system to adaptively focus on the most relevant information for decision-making while preserving generaliability. To tackle this problem, we introduce an innovative causal approach for decomposing the state and extracting Causal-InDispensable State Representations (CIDS) in RLRS. Our method concentrates on identifying the Directly Action-Influenced State Variables (DAIS) and Action-Influence Ancestors (AIA), which are essential for making effective recommendations. By leveraging conditional mutual information, we develop a framework that not only discerns the causal relationships within the generative process but also isolates critical state variables from the typically dense and high-dimensional state representations. We provide theoretical evidence for the identifiability of these variables. Then, by making use of the identified causal relationship, we construct causal-indispensable state representations, enabling the training of policies over a more advantageous subset of the agent's state space. We demonstrate the efficacy of our approach through extensive experiments, showcasing our method outperforms state-of-the-art methods.
COIN: Chance-Constrained Imitation Learning for Safe and Adaptive Resource Oversubscription under Uncertainty
Authors: Lu Wang, Mayukh Das, Fangkai Yang, Chao Du, Bo Qiao, Hang Dong, Chetan Bansal, Si Qin, Saravan Rajmohan, Qingwei Lin, Dongmei Zhang, Qi Zhang
Venue: CIKM
Year: 2024

We address the real problem of safe, robust, adaptive resource oversubscription in uncertain environments with our proposed novel technique of chance-constrained imitation learning. Our objective is to enhance resource efficiency while ensuring safety against congestion risk. Traditional supervised or forecasting models are ineffective in learning adaptive oversubscription policies, and conventional online optimization or reinforcement learning is difficult to deploy on real systems. Offline policy learning methods, such as Imitation Learning (IL) can leverage historical resource utilization telemetry data to learn effective policies if we can ensure robustness and safety from the underlying uncertainty in the domain, and thus the data. Our work investigates the nature of this uncertainty, how it can be quantified and proposes a novel chance-constrained IL that implicitly models such uncertainty in a principled manner via additional knowledge in the form of stochastic constraints on the associated risk, to learn provably safe and robust policies. We show empirically a substantial improvement (~ 3-4×) in capacity efficiency and congestion safety in test as well as real deployments.
Inferring Information Diffusion Networks without Timestamps
Authors: Yuchen Wang, Dongpeng Hou, Chao Gao, Xianghua Li, Zhen Wang
Venue: CIKM
Year: 2024

The topology of diffusion networks plays an essential role in understanding information propagation dynamics and conducting social network analysis. However, diffusion networks are often unobservable in practical applications, leading to wide research on network inference from information cascades over the past decade. At present, novel cascades-based methods have been further developed to recover temporal dynamics and network topology by exploring the utilization of node temporal information, resulting in notable advancements. However, it requires high costs to acquire extensive temporal information, and the performance of network inference may decrease due to potential observational errors. Therefore, this paper specifically focuses on the time-independent scenario to address these limitations. Firstly, this paper models the node statuses of each diffusion process by leveraging the assumption of propagation trees based on the well-known independent cascade model. Subsequently, a gradient-based approach is developed to estimate the influences between nodes, facilitating the inference of network structure. Furthermore, this paper proposes a Monte Carlo EM-based approach to enhance the efficiency of network inference while maintaining comparable accuracy. Extensive experiments are conducted to verify the efficiency and effectiveness of our approaches on both synthetic and real-world networks.
Enhancing Temporal and Geographical Named Entity Recognition in Chinese Ancient Texts with External Time-series Knowledge Bases
Authors: Xiaotong Wang, Xuanning Liu, Shuai Zhong, Xinming Chen, Bin Wu
Venue: CIKM
Year: 2024

In the field of ancient Chinese text, extracting and analysing temporal and geographic information are crucial for understanding the personal experiences of historical figures, the development of historical events, and the overall historical background. Currently, named entity recognition(NER) strategies such as BERT+CRF are used to extract temporal and geographic information from ancient Chinese text. However, ancient Chinese text covers a vast time span, and the temporal and geographic entities constantly evolve and change, making it difficult to extract these entities from text. This paper proposes a temporal and geographic extraction model for ancient Chinese text, enhanced by time-series external knowledge base. The extraction of proprietary nouns and general structures are divided into two independent networks. An external database is applied to enhance extraction of proprietary nouns and reduce noise for general structure inference. We constructed address trees and chronological tables containing commonly used places and time-related keywords from different periods and collected 12,000 texts spanning 3,000 years for extensive training. Overall, our research highlights the importance of external knowledge base for ancient Chinese NER, and provides new ideas for research in related fields.