ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 是一本学术期刊，以多学科的视角发表关于智能系统、适用算法和技术的最高质量论文。智能系统是使用人工智能 (AI) 技术提供重要服务（例如，作为更大系统的组件）以允许集成系统在现实世界中感知、推理、学习和智能行动的系统。ACM TIST 是每季出版（一年六期）。每期有 8-11 篇常规论文，大约有 20 个已发表的期刊页面或每篇论文 10,000 字。额外的参考文献、证明、图表或详细的实验结果可以作为单独的附录提交，而过长的论文将被自动拒绝。作者可以在其已发表论文的其他内容中包含仅在线附录，并鼓励与其他读者共享其代码和/或数据。

ACM Transactions on Intelligent Systems and Technology is an academic journal that publishes high-quality papers on intelligent systems, applicable algorithms, and technologies from a multidisciplinary perspective. Intelligent systems use artificial intelligence (AI) technologies to provide essential services, functioning as components of larger systems, enabling integrated systems to perceive, reason, learn, and act intelligently in the real world. ACM TIST is published quarterly, with six issues per year, each containing 8 to 11 regular papers, approximately 20 published journal pages, or up to 10,000 words per paper. Additional references, proofs, charts, or detailed experimental results may be submitted as separate appendices, and excessively long papers will be automatically rejected. Authors are allowed to include online-only appendices in their published papers and are encouraged to share their code and/or data with other readers.

《ACM Transactions on Intelligent Systems and Technology》是一本学术期刊，采用多学科的视角，发表关于智能系统、相关算法和技术的高质量论文。智能系统利用人工智能（AI）技术提供重要服务，作为更大系统的组成部分，使得集成系统能够在现实世界中进行感知、推理、学习和智能行动。该期刊每季度出版六期，每期包含8到11篇常规论文，约20页的已发表期刊内容，或每篇论文不超过10,000字。额外的参考文献、证明、图表或详细的实验结果可以作为附录提交，过长的论文将被自动拒绝。作者可以在已发表论文中包含仅在线附录，并鼓励与其他读者共享其代码和/或数据。

ACM Transactions on Intelligent Systems and Technology

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate , where to evaluate , and how to evaluate . Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the ‘where’ and ‘how’ questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey

Yupeng Chang

Xu Wang

Jindong Wang

Yuan Wu

Linyi Yang

Kaijie Zhu

Hao Chen

Xiaoyuan Yi

Cunxiang Wang

Yidong Wang

Wei Ye

Yue Zhang

Yi Chang

Philip S. Yu

Qiang Yang

Xing Xie

ACM Trans. Intell. Syst. Technol.

A Survey on Evaluation of Large Language Models

大型语言模型评估综述

Traditional recommender systems aim to estimate a user’s rating to an item based on observed ratings from the population. As with all observational studies, hidden confounders, which are factors that affect both item exposures and user ratings, lead to a systematic bias in the estimation. Consequently, causal inference has been introduced in recommendations to address the influence of unobserved confounders. Observing that confounders in recommendations are usually shared among items and are therefore multi-cause confounders, we model the recommendation as a multi-cause multi-outcome (MCMO) inference problem. Specifically, to remedy the confounding bias, we estimate user-specific latent variables that render the item exposures independent Bernoulli trials. The generative distribution is parameterized by a DNN with factorized logistic likelihood and the intractable posteriors are estimated by variational inference. Controlling these factors as substitute confounders, under mild assumptions, can eliminate the bias incurred by multi-cause confounders. Furthermore, we show that MCMO modeling may lead to high variance due to scarce observations associated with the high-dimensional treatment space. Therefore, we theoretically demonstrate that controlling user features as pre-treatment variables can substantially improve sample efficiency and alleviate overfitting. Empirical studies on both simulated and real-world datasets demonstrate that the proposed deep causal recommender shows more robustness to unobserved confounders than state-of-the-art causal recommenders. Codes and datasets are released at https://github.com/yaochenzhu/Deep-Deconf.

Yaochen Zhu

Jing Yi

Jiayi Xie

Zhenzhong Chen

Deep Causal Reasoning for Recommendations

Wangsheng Zhang

Guande Qi

Gang Pan

Hua Lu

Shijian Li

Zhaohui Wu

City-Scale Social Event Detection and Evaluation with Taxi Traces

城市规模社交事件检测与评估：基于出租车轨迹数据

Session-based recommendation (SBR) systems, traditionally reliant on complex graph neural networks (GNNs), often face challenges with marginal performance improvements despite increased model complexity. In this paper, we dissect the classical GNN-based SBR models and empirically find that the sophisticated GNN propagations might be redundant, given the readout module plays a significant role in GNN-based models. Based on this observation, we introduce Atten-Mixer+, an advanced iteration of our previously developed Multi-Level Attention Mixture Network (Atten-Mixer). Atten-Mixer+ forgoes GNN propagation in favor of a dynamic and adaptive readout process, tailored to the unique characteristics of each session. Different from the vanilla version, Atten-Mixer+ features the Adaptive Intent Scaler (AIS) layer, which dynamically determines the depth of multi-level user intent analysis, and a soft allocation approach for generating user intent queries across entire user interaction sequences. This innovative design allows Atten-Mixer+ to capture a nuanced and comprehensive understanding of user behaviors, overcoming the limitations of fixed-length analysis. Empirical evaluations on benchmark datasets highlight Atten-Mixer+'s superior efficiency and effectiveness, marking a significant step forward in the predictive accuracy of session-based recommendation systems.

Peiyan Zhang

Jiayan Guo

Chaozhuo Li

Liying Kang

Jaeboum Kim

Jie Xu

Xi Zhang

Yan Zhang

Haohan Wang

Sunghun Kim

Advancing Session-Based Recommendations with Atten-Mixer+: Dynamic and Adaptive Multi-Level Intent Mining

推进基于会话的推荐系统：Atten-Mixer+——动态自适应多层次意图挖掘

Tables are powerful and popular tools for organizing and manipulating data. A vast number of tables can be found on the Web, which represent a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.

Shuo Zhang

Krisztian Balog

Web Table Extraction, Retrieval, and Augmentation

网页表格提取、检索与增强

Heterogeneous graph neural network (HGNN) is a popular technique for modeling and analyzing heterogeneous graphs. Most existing HGNN-based approaches are supervised or semi-supervised learning methods requiring graphs to be annotated, which is costly and time-consuming. Self-supervised contrastive learning has been proposed to address the problem of requiring annotated data by mining intrinsic properties in the given data. However, the existing contrastive learning methods are not suitable for heterogeneous graphs because they construct contrastive views only based on data perturbation or pre-defined structural properties (e.g., meta-path) in graph data while ignoring noises in node attributes and graph topologies. We develop a robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidances of node attributes and graph topologies and integrates and enhances them by a reciprocally contrastive mechanism to better model heterogeneous graphs. In this new approach, we adopt distinct but suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately. We further use both attribute similarity and topological correlation to construct high-quality contrastive samples. Extensive experiments on four large real-world heterogeneous graphs demonstrate the superiority and robustness of HGCL over several state-of-the-art methods.

Cuiying Huo

Dongxiao He

Yawen Li

Di Jin

Jianwu Dang

Witold Pedrycz

Lingfei Wu

Weixiong Zhang

Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning

使用自监督互惠对比学习的异构图神经网络

Outlier detection is one of the hot topics in the field of machine learning and data mining. At present, there are many kinds of outlier detection algorithms. The accuracies of traditional outlier detection algorithms are often affected by unique parameters, and an increase in the amount of data and the dimensions of the data can seriously affect their efficiency and effectiveness. Methods based on generative adversarial networks (GANs) can solve the above problems, but they are unacceptable since the model often collapses during the training period. In this paper, to solve the problems of curse of dimensionality and model collapse, we propose a novel reinforced noise discriminator (RND) method for unsupervised outlier detection in tabular data. We consider outlier detection as a binary classification problem. Thus, we apply a learnable reinforced discriminator and generate a large number of potential outliers with a uniform distribution and potential outliers that are close to the original data that are used as a negative sample to train the discriminator, which learns the distribution of the original data to detect outliers. We empirically compare the proposed approach with ten state-of-the-art outlier detection methods on both synthetic and real-world tabular datasets. The experimental results show that RND outperforms its competitors in the majority of cases. The codes used to perform the experiments described in this paper are available at https://github.com/urlhearts/r-n-d .

Zhongping Zhang

Daoheng Liu

Jinwei Zhu

Youxi Wu

Unsupervised Outlier Detection with Reinforced Noise Discriminator

无监督异常检测与强化噪声鉴别器

Crowd flows prediction is an important problem for traffic management and public safety. Graph Convolutional Network (GCN), known for its ability to effectively capture and utilize topological information, has demonstrated significant advancements in addressing this problem. However, GCN-based models were often based on predefined crowd-flow graphs via historical movement behaviors of human beings and traffic vehicles, which ignored the abnormal changes in crowd flows. In this study, we propose a multi-scale fusion GCN-based framework with Tucker decomposition named mTDNet to enhance dynamic GCN for Crowd flows prediction. Following the paradigm of extant methods, we also employ the predefined crowd-flow graphs as a part of mTDNet to effectively capture the historical movement behaviors of crowd flows. To capture the abnormal changes, we propose a Tucker decomposition-based network with the product of the adjacency matrix of historical movement pattern graphs and an adaptive learning tensor ( \(ALT\) ) by reconstructing the crowd flows. Particularly, we utilize the Tucker decomposition scheme to decompose \(ALT\) , which enhances the dynamic learning of graph structures, allowing for effective capturing of the dynamic changes in crowd flow, including abnormal changes. Furthermore, a multi-scale three-dimensional GCN is utilized to mine and fuse the multiscale spatio-temporal information from crowd flows, to further boost the mTDNet prediction performance. Experiments conducted on two real-world datasets showed that the proposed mTDNet surpasses other crowd flow prediction methods.

Genan Dai

Weiyang Kong

Yubao Liu

Bowen Zhang

Xiaojiang Peng

Xiaomao Fan

Hu Huang

Tucker Decomposition Enhanced Dynamic Graph Convolutional Networks for Crowd Flows Prediction

基于Tucker分解增强的动态图卷积网络的人群流量预测

Can a language model predict for which questions another language model will answer successfully? We investigate the extent to which performance prediction is possible and dissect various factors that influence it. Our experimental setting fine-tunes DeBERTa models, which we call assessors , on the evaluation results of generative language models with up to 128 billion parameters, which we refer to as subject systems . Our analysis spans more than 100 tasks from BIG-bench. We find that the assessors can match and even exceed the subjects’ confidence in both refinement and calibration, anticipating failures at near perfect levels for some tasks. We also find that for performance prediction it can be beneficial to learn from the scores on multiple tasks or to learn from the scores of multiple subjects, but both depend on the task at hand. Lastly, we find that large and small subject systems are equally predictable, showing promise for the scalability of the predictability problem.

Wout Schellaert

Fernando Martínez-Plumed

José Hernández-Orallo

Analysing the Predictability of Language Model Performance

分析语言模型性能的可预测性

The ubiquitous availability of smartphones and smartwatches with integrated inertial measurement units (IMUs) enables straightforward capturing of human activities through collecting movement data. For specific applications of sensor based human activity recognition (HAR), however, logistical challenges and burgeoning costs render especially the ground truth annotation of such data a difficult endeavor, resulting in limited scale and diversity of datasets available for deriving effective HAR systems and less than ideal recognition capabilities. Transfer learning, i.e., leveraging publicly available labeled datasets to first learn useful representations that can then be fine-tuned using limited amounts of labeled data from a target domain, can alleviate some of the performance issues of contemporary HAR systems. Yet they can fail when the differences between source and target conditions are too large and / or only few samples from a target application domain are available – each of which are typical challenges in real-world human activity recognition scenarios. In this paper, we present an approach for economic use of publicly available labeled HAR datasets for effective transfer learning. We introduce a novel transfer learning framework–Cross-Domain HAR–which follows the teacher-student self-training paradigm to more effectively recognize activities with very limited label information. It bridges conceptual gaps between source and target domains, including sensor locations and type of activities. Cross-Domain HAR enables substantial performance improvements over the state-of-the-art in sensor-based HAR scenarios. Through our extensive experimental evaluation on a range of benchmark datasets we specifically demonstrate the effectiveness of our approach for practically relevant few shot activity recognition scenarios. We also present a detailed analysis into how the individual components of our framework affect downstream performance and provide practical suggestions for using the framework in real-world applications.

Megha Thukral

Harish Haresamudram

Thomas Plötz

Cross-Domain HAR: Few Shot Transfer Learning for Human Activity Recognition

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Abbreviation	ACM Trans. Intell. Syst. Technol.
Journal Impact	7.08
Quartiles(Global)	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q1)

ISSN	2157-6904, 2157-6912
h-index	78

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2010，Proportion of original research papers: 99.00%，Self Citation Rate:4.20%， Gold OA Rate: 1.69%

ACM Transactions on Intelligent Systems and Technology

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