Social simulation has long played a crucial role in exploring the mechanisms underlying human behavior and societal structures. Traditional social simulation relies on rule-based or statistical models, which makes it difficult to capture the complexity and variability of the real world. With the emergence and rapid development of large language model (LLM), new frontiers have been opened toward leveraging LLMs as agent to model human behavior and interactions. This cutting-edge direction has gained significant attention and demonstrated promising results, not only advancing research across a wide range of social science disciplines, but also enabling practical applications in role-playing scenarios. However, this field still faces multiple challenges, such as capturing real-world social phenomena, eliminating bias or ethical considerations, and ensuring usability and reliability. This workshop on LLM Agent for Social Simulation (LASS) aims to bring together researchers and practitioners from diverse backgrounds to foster interdisciplinary collaboration, address key challenges, explore new technologies, and chart promising future directions in this rapidly evolving field.

Large language models (LLMs) have achieved remarkable progress across various domains, yet their tendency to generate hallucinations remains a critical barrier to their practical reliability.Confidence calibration addresses this challenge by aligning a model's confidence with its actual accuracy, improving self-evaluation and trustworthiness.However, traditional confidence calibration, operating at response level, are inadequate for long-form generation, which involve complex outputs composed of multiple atomic facts, each with varying confidence, correctness, and relevance to the query.To overcome this limitation, we propose a fact-level confidence calibration framework that evaluates and adjusts confidence at the granularity of individual facts, incorporating both relevance and correctness.This framework identifies finer-grained calibration discrepancies, reduces overconfidence, and reveals confidence variance.Based on this framework, we introduce CARE (Confidence-Aware Fact Correction), a method that leverages high-confidence facts to iteratively refine and correct low-confidence ones.Experimental results demonstrate that our CARE effectively improves the quality of generated content.Our code is available at this link.

Heterophilic Graph Neural Networks (HGNNs) have shown promising results for semi-supervised learning tasks on graphs. Notably, most real-world heterophilic graphs are composed of a mixture of nodes with different neighbor patterns, exhibiting local node-level homophilic and heterophilic structures. However, existing works are only devoted to designing better unified HGNN backbones for node classification tasks on heterophilic and homophilic graphs simultaneously, and their analyses of HGNN performance concerning nodes are only based on the determined data distribution without exploring the effect caused by the difference of structural pattern between training and testing nodes. How to learn invariant node representations on heterophilic graphs to handle this structure difference or distribution shifts remains unexplored. In this paper, we first discuss the limitations of previous graph-based invariant learning methods in addressing the heterophilic graph structure distribution shifts from the perspective of data augmentation. Then, we propose HEI, a framework capable of generating invariant node representations through incorporating Heterophily information, the node's estimated neighbor pattern, to infer latent Environments without augmentation, which are then used for Invariant prediction. We provide detailed theoretical guarantees to clarify the reasonability of HEI. Extensive experiments on various benchmarks and backbones can also demonstrate the effectiveness and robustness of our method compared with existing state-of-the-art baselines.

Graph neural networks have shown great ability in representation (GNNs) learning on graphs, facilitating various tasks. Despite their great performance in modeling graphs, recent works show that GNNs tend to inherit and amplify the bias from training data, causing concerns of the adoption of GNNs in high-stake scenarios. Hence, many efforts have been taken for fairness-aware GNNs. However, most existing fair GNNs learn fair node representations by adopting statistical fairness notions, which may fail to alleviate bias in the presence of statistical anomalies. Motivated by causal theory, there are several attempts utilizing graph counterfactual fairness to mitigate root causes of unfairness. However, these methods suffer from non-realistic counterfactuals obtained by perturbation or generation. In this paper, we take a causal view on fair graph learning problem. Guided by the casual analysis, we propose a novel framework CAF, which can select counterfactuals from training data to avoid non-realistic counterfactuals and adopt selected counterfactuals to learn fair node representations for node classification task. Extensive experiments on synthetic and real-world datasets show the effectiveness of CAF. Our code is available at https://github.com/TimeLovercc/CAF-GNN.

This work studies the problem of learning unbiased algorithms from biased feedback for recommendation. We address this problem from a novel distribution shift perspective. Recent works in unbiased recommendation have advanced the state-of-the-art with various techniques such as re-weighting, multi-task learning, and meta-learning. Despite their empirical successes, most of them lack theoretical guarantees, forming non-negligible gaps between theories and recent algorithms. In this paper, we propose a theoretical understanding of why existing unbiased learning objectives work for unbiased recommendation. We establish a close connection between unbiased recommendation and distribution shift, which shows that existing unbiased learning objectives implicitly align biased training and unbiased test distributions. Built upon this connection, we develop two generalization bounds for existing unbiased learning methods and analyze their learning behavior. Besides, as a result of the distribution shift, we further propose a principled framework, Adversarial Self-Training (AST), for unbiased recommendation. Extensive experiments on real-world and semi-synthetic datasets demonstrate the effectiveness of AST.

The logged feedback for training recommender systems is usually subject to selection bias, which could not reflect real user preference. Thus, many efforts have been made to learn the de-biased recommender system from biased feedback. However, existing methods for dealing with selection bias are usually affected by the error of propensity weight estimation, have high variance, or assume access to uniform data, which is expensive to be collected in practice. In this work, we address these issues by proposing Learning De-biased Representations (LDR), a framework derived from the representation learning perspective. LDR bridges the gap between propensity weight estimation (WE) and unbiased weighted learning (WL) and provides an end-to-end solution that iteratively conducts WE and WL. We show LDR can effectively alleviate selection bias with bounded variance. We also perform theoretical analysis on the statistical properties of LDR, such as its bias, variance, and generalization performance. Extensive experiments on both semi-synthetic and real-world datasets demonstrate the effectiveness of LDR.

Graph Neural Networks (GNNs) show promising results for semi-supervised learning tasks on graphs, which become favorable comparing with other approaches. However, similar to other machine learning models, GNN s might suffer from the bias issue because of the distribution shift between training and testing node distributions. More importantly, the test node distribution in the graph is generally unknown during model training in practice. In this paper, we focus on how to address the bias issue on graphs and learn a graph neural network model that is robust to arbitrary unknown distribution shifts. To address this problem, we propose a novel Bias-Aware Graph Neural Network (BA-GNN) framework by learning node representations that are invariant across different distributions for invariant prediction. Specifically, our BA-GNN framework contains two interactive parts, one for bias identification and the other for invariant prediction. To learn invariant feature and aggregated representation, our BA-GNN learns multiple biased graph partitions and selects feature, neighbor, and propagation steps for nodes under multiple biased graph partitions. Extensive experiments show that our proposed BA-G NN framework can significantly improve different GNNs backbones such as GCN, GAT, APPNP and GraphSAGE on different datasets.

We study the problem of optimizing ranking metrics with unbiased and robust causal estimation for recommender systems. A user may click/purchase an item regardless of whether the item is recommended or not. Thus, it is important to estimate the causal effect of recommendation and rank items higher with a larger causal effect. However, most existing works focused on improving the accuracy of recommendations, which usually have large bias and variance. Therefore, in this paper, we provide a general and theoretically rigorous framework for causal recommender systems, which enables unbiased evaluation and learning for the ranking metrics with confounding bias. We first propose a robust estimator for unbiased ranking evaluation and theoretically show that this estimator has a smaller bias and variance. We then propose a deep variational information bottleneck (IB) approach to exploit the sufficiency of the propensity score for estimation adjustment and better generalization. We also provide the learning bound and develop an unbiased learning algorithm to optimize the causal metric. Results on semi-synthetic and real-world datasets show that our evaluation and learning algorithms significantly outperform existing methods.

This paper studies the problem of learning message propagation strategies for graph neural networks (GNNs). One of the challenges for graph neural networks is that of defining the propagation strategy. For instance, the choices of propagation steps are often specialized to a single graph and are not personalized to different nodes. To compensate for this, in this paper, we present learning to propagate, a general learning framework that not only learns the GNN parameters for prediction but more importantly, can explicitly learn the interpretable and personalized propagate strategies for different nodes and various types of graphs. We introduce the optimal propagation steps as latent variables to help find the maximum-likelihood estimation of the GNN parameters in a variational Expectation- Maximization (VEM) framework. Extensive experiments on various types of graph benchmarks demonstrate that our proposed frame- work can significantly achieve better performance compared with the state-of-the-art methods, and can effectively learn personalized and interpretable propagate strategies of messages in GNNs.

In this paper, we provide a unified learning algorithm, dynamic collaborative recurrent learning, DCRL, of two directions of recommendations: temporal recommendations focusing on tracking the evolution of users' long-term preference and sequential recommendations focusing on capturing short-term preferences given a short time window. Our DCRL builds based on RNN and Sate Space Model (SSM), and thus it is not only able to collaboratively capture users' short-term and long-term preferences as in sequential recommendations, but also can dynamically track the evolution of users' long-term preferences as in temporal recommendations in a unified framework. In addition, we introduce two smoothing and filtering scalable inference algorithms for DCRL's offline and online learning, respectively, based on amortized variational inference, allowing us to effectively train the model jointly over all time. Experiments demonstrate DCRL outperforms the temporal and sequential recommender models, and does capture users' short-term preferences and track the evolution of long-term preferences.

In this paper, we study the problem of personalized product search under streaming scenarios. We address the problem by proposing a Dynamic Bayesian Metric Learning model, abbreviated as DBML, which can collaboratively track the evolutions of latent semantic representations of different categories of entities (i.e., users, products and words) over time in a joint metric space. In particular, unlike previous work using inner-product metric to model the affinities between entities, our DBML is a novel probabilistic metric learning approach that is able to avoid the contradicts, keep the triangle inequality in the latent space, and correctly utilize implicit feedbacks. For inferring dynamic embeddings of the entities, we propose a scalable online inference algorithm, which can jointly learn the latent representations of entities and smooth their changes across time, based on amortized inference. The inferred dynamic semantic representations of entities collaboratively inferred in a unified form by our DBML can benefit not only for improving personalized product search, but also for capturing the affinities between users, products and words. Experimental results on large datasets over a number of applications demonstrate that our DBML outperforms the state-of-the-art algorithms, and can effectively capture the evolutions of semantic representations of different categories of entities over time.

In this paper, we study the problem of recommending personalized items to users given their sequential behaviors. Most sequential recommendation models only capture a user's short-term preference in a short session, and neglect his general (unchanged over time) and long-term preferences. Besides, they are all based on deterministic neural networks, and consider users' latent preferences as point vectors in a low-dimensional continuous space. However, in real world, the evolutions of users' preferences are full of uncertainties. We address this problem by proposing a hierarchical neural variational model (HNVM). HNVM models users' three preferences: general, long-term and short-term preferences through an unified hierarchical deep generative process. HNVM is a hierarchical recurrent neural network that enables it to capture both user's long-term and short-term preferences. Experiments on two public datasets demonstrate that HNVM outperforms state-of-the-art sequential recommendation methods.