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Found 39 papers, 10 papers with code
Mitigating Popularity Bias in Collaborative Filtering through Fair Sampling
Recommender systems often suffer from popularity bias, where frequently interacted items are overrepresented in recommendations.
Enhancing Cross-Domain Recommendations with Memory-Optimized LLM-Based User Agents
Large Language Model (LLM)-based user agents have emerged as a powerful tool for improving recommender systems by simulating user interactions.
Enhancing LLM-Based Recommendations Through Personalized Reasoning
Current recommendation systems powered by large language models (LLMs) often underutilize their reasoning capabilities due to a lack of explicit logical structuring.
Predictor-Corrector Enhanced Transformers with Exponential Moving Average Coefficient Learning
First, we introduce a predictor-corrector learning framework to minimize truncation errors, which consists of a high-order predictor and a multistep corrector.
FIRP: Faster LLM inference via future intermediate representation prediction
Recent advancements in Large Language Models (LLMs) have shown remarkable performance across a wide range of tasks.
Filtering Discomforting Recommendations with Large Language Models
Personalized algorithms can inadvertently expose users to discomforting recommendations, potentially triggering negative consequences.
M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning
Instruction tuning has emerged as an effective strategy for achieving zero-shot generalization by finetuning pretrained models on diverse multimodal tasks.
ReMamba: Equip Mamba with Effective Long-Sequence Modeling
While the Mamba architecture demonstrates superior inference efficiency and competitive performance on short-context natural language processing (NLP) tasks, empirical evidence suggests its capacity to comprehend long contexts is limited compared to transformer-based models.
UAV-Enabled Integrated Sensing and Communication in Maritime Emergency Networks
Once the UAV passes the initial operating position, the UAV's trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS.
A Probabilistic Approach for Queue Length Estimation Using License Plate Recognition Data: Considering Overtaking in Multi-lane Scenarios
Eventually, to leverage the LPR data sufficiently, we extend our approach to multi-lane scenarios, where the problem can be converted to a weighted general exact coverage problem and solved by a backtracking algorithm with heuristics.
Graph-Structured Speculative Decoding
We apply GSD across a range of LLMs, including a 70-billion parameter LLaMA-2 model, and observe a remarkable speedup of 1. 73$\times$ to 1. 96$\times$, significantly surpassing standard speculative decoding.
EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction
Specifically, we employ a heavy encoder to separately encode the product context and attribute.
Speculative Decoding via Early-exiting for Faster LLM Inference with Thompson Sampling Control Mechanism
The recent advancements in large language models (LLMs) have been extraordinary, yet the escalating inference costs associated with them present challenges in real-world applications.
Parallel Decoding via Hidden Transfer for Lossless Large Language Model Acceleration
In this paper, we propose a novel parallel decoding approach, namely \textit{hidden transfer}, which decodes multiple successive tokens simultaneously in a single forward pass.
What Makes Quantization for Large Language Models Hard? An Empirical Study from the Lens of Perturbation
Our findings reveal several connections between the properties of perturbations and LLM performance, providing insights into the failure cases of uniform quantization and suggesting potential solutions to improve the robustness of LLM quantization.
C-ICL: Contrastive In-context Learning for Information Extraction
There has been increasing interest in exploring the capabilities of advanced large language models (LLMs) in the field of information extraction (IE), specifically focusing on tasks related to named entity recognition (NER) and relation extraction (RE).
Unraveling the Key of Machine Learning Solutions for Android Malware Detection
Android malware detection serves as the front line against malicious apps.
View Distribution Alignment with Progressive Adversarial Learning for UAV Visual Geo-Localization
Previous works map images captured by UAVs and satellites to a shared feature space and employ a classification framework to learn location-dependent features while neglecting the overall distribution shift between the UAV view and the satellite view.
Improving Input-label Mapping with Demonstration Replay for In-context Learning
The effectiveness of ICL can be attributed to the strong language modeling capabilities of large language models (LLMs), which enable them to learn the mapping between input and labels based on in-context demonstrations.
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression
In particular, our approach extracts knowledge from LLMs to construct a knowledge store, from which the small-scale model can retrieve relevant information and leverage it for effective inference.
mCL-NER: Cross-Lingual Named Entity Recognition via Multi-view Contrastive Learning
A multi-view contrastive learning framework is introduced to encompass semantic contrasts between source, codeswitched, and target sentences, as well as contrasts among token-to-token relations.
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users.
Recommendation Unlearning via Matrix Correction
We conducted comprehensive experiments to validate the effectiveness of IMCorrect and the results demonstrate that IMCorrect is superior in completeness, utility, and efficiency, and is applicable in many recommendation unlearning scenarios.
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model
Currently, the reduction in the parameter scale of large-scale pre-trained language models (PLMs) through knowledge distillation has greatly facilitated their widespread deployment on various devices.
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models
While transformer-based pre-trained language models (PLMs) have dominated a number of NLP applications, these models are heavy to deploy and expensive to use.
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank
In this paper, we propose a novel approach, RankCSE, for unsupervised sentence representation learning, which incorporates ranking consistency and ranking distillation with contrastive learning into a unified framework.
Lifting the Curse of Capacity Gap in Distilling Language Models
However, when the capacity gap between the teacher and the student is large, a curse of capacity gap appears, invoking a deficiency in distilling LMs.
FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment
In this paper, we propose a new PFL algorithm called \emph{FedDWA (Federated Learning with Dynamic Weight Adjustment)} to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients.
Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation
Specifically, TriSIM4Rec consists of 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrence information in user-item interactions, which is implemented by incremental singular value decomposition (SVD); 2) a parameter-free attention module to capture sequential information of user interactions effectively and efficiently; and 3) an item transition matrix to store the transition probabilities of item pairs.
Multi-task Transformer with Relation-attention and Type-attention for Named Entity Recognition
There are three types of NER tasks, including flat, nested and discontinuous entity recognition.
Personalized Graph Signal Processing for Collaborative Filtering
However, the interaction signal may not be sufficient to accurately characterize user interests and the low-pass filters may ignore the useful information contained in the high-frequency component of the observed signals, resulting in suboptimal accuracy.
Parameter-free Dynamic Graph Embedding for Link Prediction
Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time.
MiniDisc: Minimal Distillation Schedule for Language Model Compression
In particular, motivated by the finding that the performance of the student is positively correlated to the scale-performance tradeoff of the teacher assistant, MiniDisc is designed with a $\lambda$-tradeoff to measure the optimality of the teacher assistant without trial distillation to the student.
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval
To enhance the performance of dense retrieval models without loss of efficiency, we propose a GNN-encoder model in which query (passage) information is fused into passage (query) representations via graph neural networks that are constructed by queries and their top retrieved passages.
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation
Existing work in multilingual pretraining has demonstrated the potential of cross-lingual transferability by training a unified Transformer encoder for multiple languages.
VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation
Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks.
Community-preserving Graph Convolutions for Structural and Functional Joint Embedding of Brain Networks
Specifically, we use graph convolutions to learn the structural and functional joint embedding, where the graph structure is defined with structural connectivity and node features are from the functional connectivity.
A Planning based Framework for Essay Generation
Generating an article automatically with computer program is a challenging task in artificial intelligence and natural language processing.
