Piecing It All Together: Verifying Multi-Hop Multimodal Claims

Existing claim verification datasets often do not require systems to perform complex reasoning or effectively interpret multimodal evidence. To address this, we introduce a new task: multi-hop multimodal claim verification. This task challenges models to reason over multiple pieces of evidence from diverse sources, including text, images, and tables, and determine whether the combined multimodal evidence supports or refutes a given claim. To study this task, we construct MMCV, a large-scale dataset comprising 16k multi-hop claims paired with multimodal evidence, generated and refined using large language models, with additional input from human feedback. We show that MMCV is challenging even for the latest state-of-the-art multimodal large language models, especially as the number of reasoning hops increases. Additionally, we establish a human performance benchmark on a subset of MMCV. We hope this dataset and its evaluation task will encourage future research in multimodal multi-hop claim verification.

Taxonomy-Guided Zero-Shot Recommendations with LLMs

With the emergence of large language models (LLMs) and their ability to perform a variety of tasks, their application in recommender systems (RecSys) has shown promise. However, we are facing significant challenges when deploying LLMs into RecSys, such as limited prompt length, unstructured item information, and un-constrained generation of recommendations, leading to sub-optimal performance. To address these issues, we propose a novel method using a taxonomy dictionary. This method provides a systematic framework for categorizing and organizing items, improving the clarity and structure of item information. By incorporating the taxonomy dictionary into LLM prompts, we achieve efficient token utilization and controlled feature generation, leading to more accurate and contextually relevant recommendations. Our Taxonomy-guided Recommendation (TaxRec) approach features a two-step process: one-time taxonomy categorization and LLM-based recommendation, enabling zero-shot recommendations without the need for domain-specific fine-tuning. Experimental results demonstrate TaxRec significantly enhances recommendation quality compared to traditional zero-shot approaches, showcasing its efficacy as personal recommender with LLMs. Code is available at https://github.com/yueqingliang1/TaxRec.

Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting

Time series forecasting is an important problem and plays a key role in a variety of applications including weather forecasting, stock market, and scientific simulations. Although transformers have proven to be effective in capturing dependency, its quadratic complexity of attention mechanism prevents its further adoption in long-range time series forecasting, thus limiting them attend to short-range range. Recent progress on state space models (SSMs) have shown impressive performance on modeling long range dependency due to their subquadratic complexity. Mamba, as a representative SSM, enjoys linear time complexity and has achieved strong scalability on tasks that requires scaling to long sequences, such as language, audio, and genomics. In this paper, we propose to leverage a hybrid framework Mambaformer that internally combines Mamba for long-range dependency, and Transformer for short range dependency, for long-short range forecasting. To the best of our knowledge, this is the first paper to combine Mamba and Transformer architecture in time series data. We investigate possible hybrid architectures to combine Mamba layer and attention layer for long-short range time series forecasting. The comparative study shows that the Mambaformer family can outperform Mamba and Transformer in long-short range time series forecasting problem. The code is available at https://github.com/XiongxiaoXu/Mambaformerin-Time-Series.

Confidence-aware Fine-tuning of Sequential Recommendation Systems via Conformal Prediction

In Sequential Recommendation Systems, Cross-Entropy (CE) loss is commonly used but fails to harness item confidence scores during training. Recognizing the critical role of confidence in aligning training objectives with evaluation metrics, we propose CPFT, a versatile framework that enhances recommendation confidence by integrating Conformal Prediction (CP)-based losses with CE loss during fine-tuning. CPFT dynamically generates a set of items with a high probability of containing the ground truth, enriching the training process by incorporating validation data without compromising its role in model selection. This innovative approach, coupled with CP-based losses, sharpens the focus on refining recommendation sets, thereby elevating the confidence in potential item predictions. By fine-tuning item confidence through CP-based losses, CPFT significantly enhances model performance, leading to more precise and trustworthy recommendations that increase user trust and satisfaction. Our extensive evaluation across five diverse datasets and four distinct sequential models confirms CPFT's substantial impact on improving recommendation quality through strategic confidence optimization. Access to the framework's code will be provided following the acceptance of the paper.

Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models

This work investigates the potential of undermining both fairness and detection performance in abusive language detection. In a dynamic and complex digital world, it is crucial to investigate the vulnerabilities of these detection models to adversarial fairness attacks to improve their fairness robustness. We propose a simple yet effective framework FABLE that leverages backdoor attacks as they allow targeted control over the fairness and detection performance. FABLE explores three types of trigger designs (i.e., rare, artificial, and natural triggers) and novel sampling strategies. Specifically, the adversary can inject triggers into samples in the minority group with the favored outcome (i.e., "non-abusive") and flip their labels to the unfavored outcome, i.e., "abusive". Experiments on benchmark datasets demonstrate the effectiveness of FABLE attacking fairness and utility in abusive language detection.

Collaborative Contextualization: Bridging the Gap between Collaborative Filtering and Pre-trained Language Model

Traditional recommender systems have heavily relied on identity representations (IDs) to model users and items, while the ascendancy of pre-trained language model (PLM) encoders has enriched the modeling of contextual item descriptions. However, PLMs, although effective in addressing few-shot, zero-shot, or unified modeling scenarios, often neglect the crucial collaborative filtering signal. This neglect gives rise to two pressing challenges: (1) Collaborative Contextualization, the seamless integration of collaborative signals with contextual representations. (2) the imperative to bridge the representation gap between ID-based representations and contextual representations while preserving their contextual semantics. In this paper, we propose CollabContext, a novel model that adeptly combines collaborative filtering signals with contextual representations and aligns these representations within the contextual space, preserving essential contextual semantics. Experimental results across three real-world datasets demonstrate substantial improvements. Leveraging collaborative contextualization, CollabContext can also be effectively applied to cold-start scenarios, achieving remarkable enhancements in recommendation performance. The code is available after the conference accepts the paper.

On Fair Classification with Mostly Private Sensitive Attributes

Machine learning models have demonstrated promising performance in many areas. However, the concerns that they can be biased against specific groups hinder their adoption in high-stake applications. Thus it is essential to ensure fairness in machine learning models. Most of the previous efforts require access to sensitive attributes for mitigating bias. Nonetheless, it is often infeasible to obtain large scale of data with sensitive attributes due to people's increasing awareness of privacy and the legal compliance. Therefore, an important research question is how to make fair predictions under privacy? In this paper, we study a novel problem on fair classification in a semi-private setting, where most of the sensitive attributes are private and only a small amount of clean sensitive attributes are available. To this end, we propose a novel framework FairSP that can first learn to correct the noisy sensitive attributes under privacy guarantee via exploiting the limited clean sensitive attributes. Then, it jointly models the corrected and clean data in an adversarial way for debiasing and prediction. Theoretical analysis shows that the proposed model can ensure fairness when most of the sensitive attributes are private. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model for making fair predictions under privacy and maintaining high accuracy.

Joint Adversarial Learning for Cross-domain Fair Classification

Modern machine learning (ML) models are becoming increasingly popular and are widely used in decision-making systems. However, studies have shown critical issues of ML discrimination and unfairness, which hinder their adoption on high-stake applications. Recent research on fair classifiers has drawn significant attention to develop effective algorithms to achieve fairness and good classification performance. Despite the great success of these fairness-aware machine learning models, most of the existing models require sensitive attributes to preprocess the data, regularize the model learning or postprocess the prediction to have fair predictions. However, sensitive attributes are often incomplete or even unavailable due to privacy, legal or regulation restrictions. Though we lack the sensitive attribute for training a fair model in the target domain, there might exist a similar domain that has sensitive attributes. Thus, it is important to exploit auxiliary information from the similar domain to help improve fair classification in the target domain. Therefore, in this paper, we study a novel problem of exploring domain adaptation for fair classification. We propose a new framework that can simultaneously estimate the sensitive attributes while learning a fair classifier in the target domain. Extensive experiments on real-world datasets illustrate the effectiveness of the proposed model for fair classification, even when no sensitive attributes are available in the target domain.

Pre-training Graph Neural Network for Cross Domain Recommendation

A recommender system predicts users' potential interests in items, where the core is to learn user/item embeddings. Nevertheless, it suffers from the data-sparsity issue, which the cross-domain recommendation can alleviate. However, most prior works either jointly learn the source domain and target domain models, or require side-features. However, jointly training and side features would affect the prediction on the target domain as the learned embedding is dominated by the source domain containing bias information. Inspired by the contemporary arts in pre-training from graph representation learning, we propose a pre-training and fine-tuning diagram for cross-domain recommendation. We devise a novel Pre-training Graph Neural Network for Cross-Domain Recommendation (PCRec), which adopts the contrastive self-supervised pre-training of a graph encoder. Then, we transfer the pre-trained graph encoder to initialize the node embeddings on the target domain, which benefits the fine-tuning of the single domain recommender system on the target domain. The experimental results demonstrate the superiority of PCRec. Detailed analyses verify the superiority of PCRec in transferring information while avoiding biases from source domains.