Knowledge Refinement via Interaction Between Search Engines and Large Language Models

Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern search engines (SEs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural language. In this paper, we explore the advantages and disadvantages of LLMs and SEs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose InteR, a novel framework that facilitates knowledge refinement through interaction between SEs and LLMs. InteR allows SEs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using SE-retrieved documents. This iterative refinement process augments the inputs of SEs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks demonstrate that InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteR

Graph-guided Personalization for Federated Recommendation

Federated Recommendation is a new service architecture providing recommendations without sharing user data with the server. Existing methods deploy a recommendation model on each client and coordinate their training by synchronizing and aggregating item embeddings. However, while users usually hold diverse preferences toward certain items, these methods indiscriminately aggregate item embeddings from all clients, neutralizing underlying user-specific preferences. Such neglect will leave the aggregated embedding less discriminative and hinder personalized recommendations. This paper proposes a novel Graph-guided Personalization framework (GPFedRec) for the federated recommendation. The GPFedRec enhances cross-client collaboration by leveraging an adaptive graph structure to capture the correlation of user preferences. Besides, it guides training processes on clients by formulating them into a unified federated optimization framework, where models can simultaneously use shared and personalized user preferences. Experiments on five benchmark datasets demonstrate GPFedRec's superior performance in providing personalized recommendations.

Large Language Models are Strong Zero-Shot Retriever

In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, Language language model as Retriever (LameR) is built upon no other neural models but an LLM, while breaking up brute-force combinations of retrievers with LLMs and lifting the performance of zero-shot retrieval to be very competitive on benchmark datasets. Essentially, we propose to augment a query with its potential answers by prompting LLMs with a composition of the query and the query's in-domain candidates. The candidates, regardless of correct or wrong, are obtained by a vanilla retrieval procedure on the target collection. Such candidates, as a part of prompts, are likely to help LLM generate more precise answers by pattern imitation or candidate summarization. Even if all the candidates are wrong, the prompts at least make LLM aware of in-collection patterns and genres. Moreover, due to the low performance of a self-supervised retriever, the LLM-based query augmentation becomes less effective as the retriever bottlenecks the whole pipeline. So, we propose to leverage a non-parametric lexicon-based method (e.g., BM25) as the retrieval module to capture query-document overlap in a literal fashion. As such, LameR makes the retrieval procedure transparent to the LLM, so it circumvents the performance bottleneck.

Does Continual Learning Equally Forget All Parameters?

Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedly replaying buffered data, the every-step replay is time-consuming. In this paper, we study which modules in neural networks are more prone to forgetting by investigating their training dynamics during CL. Our proposed metrics show that only a few modules are more task-specific and sensitively alter between tasks, while others can be shared across tasks as common knowledge. Hence, we attribute forgetting mainly to the former and find that finetuning them only on a small buffer at the end of any CL method can bring non-trivial improvement. Due to the small number of finetuned parameters, such ``Forgetting Prioritized Finetuning (FPF)'' is efficient in computation. We further propose a more efficient and simpler method that entirely removes the every-step replay and replaces them by only $k$-times of FPF periodically triggered during CL. Surprisingly, this ``$k$-FPF'' performs comparably to FPF and outperforms the SOTA CL methods but significantly reduces their computational overhead and cost. In experiments on several benchmarks of class- and domain-incremental CL, FPF consistently improves existing CL methods by a large margin, and $k$-FPF further excels in efficiency without degrading the accuracy. We also empirically studied the impact of buffer size, epochs per task, and finetuning modules on the cost and accuracy of our methods.

Neural Time Series Analysis with Fourier Transform: A Survey

Recently, Fourier transform has been widely introduced into deep neural networks to further advance the state-of-the-art regarding both accuracy and efficiency of time series analysis. The advantages of the Fourier transform for time series analysis, such as efficiency and global view, have been rapidly explored and exploited, exhibiting a promising deep learning paradigm for time series analysis. However, although increasing attention has been attracted and research is flourishing in this emerging area, there lacks a systematic review of the variety of existing studies in the area. To this end, in this paper, we provide a comprehensive review of studies on neural time series analysis with Fourier transform. We aim to systematically investigate and summarize the latest research progress. Accordingly, we propose a novel taxonomy to categorize existing neural time series analysis methods from four perspectives, including characteristics, usage paradigms, network design, and applications. We also share some new research directions in this vibrant area.

Voting from Nearest Tasks: Meta-Vote Pruning of Pre-trained Models for Downstream Tasks

As a few large-scale pre-trained models become the major choices of various applications, new challenges arise for model pruning, e.g., can we avoid pruning the same model from scratch for every downstream task? How to reuse the pruning results of previous tasks to accelerate the pruning for a new task? To address these challenges, we create a small model for a new task from the pruned models of similar tasks. We show that a few fine-tuning steps on this model suffice to produce a promising pruned-model for the new task. We study this ''meta-pruning'' from nearest tasks on two major classes of pre-trained models, convolutional neural network (CNN) and vision transformer (ViT), under a limited budget of pruning iterations. Our study begins by investigating the overlap of pruned models for similar tasks and how the overlap changes over different layers and blocks. Inspired by these discoveries, we develop a simple but effective ''Meta-Vote Pruning (MVP)'' method that significantly reduces the pruning iterations for a new task by initializing a sub-network from the pruned models of its nearest tasks. In experiments, we demonstrate MVP's advantages in accuracy, efficiency, and generalization through extensive empirical studies and comparisons with popular pruning methods over several datasets.

Style-Aware Contrastive Learning for Multi-Style Image Captioning

Existing multi-style image captioning methods show promising results in generating a caption with accurate visual content and desired linguistic style. However, existing methods overlook the relationship between linguistic style and visual content. To overcome this drawback, we propose style-aware contrastive learning for multi-style image captioning. First, we present a style-aware visual encoder with contrastive learning to mine potential visual content relevant to style. Moreover, we propose a style-aware triplet contrast objective to distinguish whether the image, style and caption matched. To provide positive and negative samples for contrastive learning, we present three retrieval schemes: object-based retrieval, RoI-based retrieval and triplet-based retrieval, and design a dynamic trade-off function to calculate retrieval scores. Experimental results demonstrate that our approach achieves state-of-the-art performance. In addition, we conduct an extensive analysis to verify the effectiveness of our method.

Improving Cross-modal Alignment for Text-Guided Image Inpainting

Text-guided image inpainting (TGII) aims to restore missing regions based on a given text in a damaged image. Existing methods are based on a strong vision encoder and a cross-modal fusion model to integrate cross-modal features. However, these methods allocate most of the computation to visual encoding, while light computation on modeling modality interactions. Moreover, they take cross-modal fusion for depth features, which ignores a fine-grained alignment between text and image. Recently, vision-language pre-trained models (VLPM), encapsulating rich cross-modal alignment knowledge, have advanced in most multimodal tasks. In this work, we propose a novel model for TGII by improving cross-modal alignment (CMA). CMA model consists of a VLPM as a vision-language encoder, an image generator and global-local discriminators. To explore cross-modal alignment knowledge for image restoration, we introduce cross-modal alignment distillation and in-sample distribution distillation. In addition, we employ adversarial training to enhance the model to fill the missing region in complicated structures effectively. Experiments are conducted on two popular vision-language datasets. Results show that our model achieves state-of-the-art performance compared with other strong competitors.

Multimodal Event Transformer for Image-guided Story Ending Generation

Image-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but overlook reasoning and mining implicit information from story plots and ending image. To tackle this drawback, we propose a multimodal event transformer, an event-based reasoning framework for IgSEG. Specifically, we construct visual and semantic event graphs from story plots and ending image, and leverage event-based reasoning to reason and mine implicit information in a single modality. Next, we connect visual and semantic event graphs and utilize cross-modal fusion to integrate different-modality features. In addition, we propose a multimodal injector to adaptive pass essential information to decoder. Besides, we present an incoherence detection to enhance the understanding context of a story plot and the robustness of graph modeling for our model. Experimental results show that our method achieves state-of-the-art performance for the image-guided story ending generation.

Federated Recommendation with Additive Personalization

With rising concerns about privacy, developing recommendation systems in a federated setting become a new paradigm to develop next-generation Internet service architecture. However, existing approaches are usually derived from a distributed recommendation framework with an additional mechanism for privacy protection, thus most of them fail to fully exploit personalization in the new context of federated recommendation settings. In this paper, we propose a novel approach called Federated Recommendation with Additive Personalization (FedRAP) to enhance recommendation by learning user embedding and the user's personal view of item embeddings. Specifically, the proposed additive personalization is to add a personalized item embedding to a sparse global item embedding aggregated from all users. Moreover, a curriculum learning mechanism has been applied for additive personalization on item embeddings by gradually increasing regularization weights to mitigate the performance degradation caused by large variances among client-specific item embeddings. A unified formulation has been proposed with a sparse regularization of global item embeddings for reducing communication overhead. Experimental results on four real-world recommendation datasets demonstrate the effectiveness of FedRAP.