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Found 34 papers, 18 papers with code
Are LLM-based Recommenders Already the Best? Simple Scaled Cross-entropy Unleashes the Potential of Traditional Sequential Recommenders
Some studies have observed that LLMs, when fine-tuned by the cross-entropy (CE) loss with a full softmax, could achieve `state-of-the-art' performance in sequential recommendation.
You Only Cache Once: Decoder-Decoder Architectures for Language Models
We introduce a decoder-decoder architecture, YOCO, for large language models, which only caches key-value pairs once.
Understanding the Role of Cross-Entropy Loss in Fairly Evaluating Large Language Model-based Recommendation
Large language models (LLMs) have gained much attention in the recommendation community; some studies have observed that LLMs, fine-tuned by the cross-entropy loss with a full softmax, could achieve state-of-the-art performance already.
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.
Learning Interpretable Rules for Scalable Data Representation and Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Graph-enhanced Optimizers for Structure-aware Recommendation Embedding Evolution
Embedding plays a key role in modern recommender systems because they are virtual representations of real-world entities and the foundation for subsequent decision-making models.
FlexKBQA: A Flexible LLM-Powered Framework for Few-Shot Knowledge Base Question Answering
Knowledge base question answering (KBQA) is a critical yet challenging task due to the vast number of entities within knowledge bases and the diversity of natural language questions posed by users.
Knowledge-aware Collaborative Filtering with Pre-trained Language Model for Personalized Review-based Rating Prediction
For the first issue, to utilize rich knowledge, KCF-PLM develops a transformer network to model the interactions of the extracted aspects w. r. t.
Retentive Network: A Successor to Transformer for Large Language Models
In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance.
Can LLMs like GPT-4 outperform traditional AI tools in dementia diagnosis? Maybe, but not today
In this paper, we explore the potential of LLMs such as GPT-4 to outperform traditional AI tools in dementia diagnosis.
Bridging the Language Gap: Knowledge Injected Multilingual Question Answering
As a popular topic in natural language processing tasks, extractive question answering task (extractive QA) has gained extensive attention in the past few years.
Optimization Techniques for Unsupervised Complex Table Reasoning via Self-Training Framework
However, constructing labeled data for complex reasoning tasks is labor intensive, and the quantity of annotated data remains insufficient to support the intricate demands of real-world applications.
Joint Open Knowledge Base Canonicalization and Linking
However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts.
Not Just Plain Text! Fuel Document-Level Relation Extraction with Explicit Syntax Refinement and Subsentence Modeling
Document-level relation extraction (DocRE) aims to identify semantic labels among entities within a single document.
Learning Entity Linking Features for Emerging Entities
Entity linking (EL) is the process of linking entity mentions appearing in text with their corresponding entities in a knowledge base.
Effective Few-Shot Named Entity Linking by Meta-Learning
In this paper, we endeavor to solve the problem of few-shot entity linking, which only requires a minimal amount of in-domain labeled data and is more practical in real situations.
Prompt Tuning for Discriminative Pre-trained Language Models
Recent works have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing (NLP) tasks.
Data-Free Knowledge Transfer: A Survey
In this paper, we provide a comprehensive survey on data-free knowledge transfer from the perspectives of knowledge distillation and unsupervised domain adaptation, to help readers have a better understanding of the current research status and ideas.
Scalable Rule-Based Representation Learning for Interpretable Classification
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Entity Linking Meets Deep Learning: Techniques and Solutions
Entity linking (EL) is the process of linking entity mentions appearing in web text with their corresponding entities in a knowledge base.
Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation
Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items.
Toward Tweet Entity Linking with Heterogeneous Information Networks
The task of linking an entity mention in a tweet with its corresponding entity in a heterogeneous information network is of great importance, for the purpose of enriching heterogeneous information networks with the abundant and fresh knowledge embedded in tweets.
Social Link Inference via Multi-View Matching Network from Spatio-Temporal Trajectories
In this paper, we investigate the problem of social link inference in a target Location-aware Social Network (LSN), which aims at predicting the unobserved links between users within the network.
Graph-Based Tri-Attention Network for Answer Ranking in CQA
However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked.
RRL: A Scalable Classifier for Interpretable Rule-Based Representation Learning
Rule-based models, e. g., decision trees, are widely used in scenarios demanding high model interpretability for their transparent inner structures and good model expressivity.
Transparent Classification with Multilayer Logical Perceptrons and Random Binarization
In this paper, we propose a new hierarchical rule-based model for classification tasks, named Concept Rule Sets (CRS), which has both a strong expressive ability and a transparent inner structure.
Attentive Representation Learning with Adversarial Training for Short Text Clustering
Relying on this, the representation learning and clustering for short texts are seamlessly integrated into a unified model.
Knowledge-Aware Deep Dual Networks for Text-Based Mortality Prediction
To address the above issues, we propose novel Knowledge-aware Deep Dual Networks (K-DDN) for the text-based mortality prediction task.
A Novel Hybrid Sequential Model for Review-based Rating Prediction
Nowadays, the online interactions between users and items become diverse, and may include textual reviews as well as numerical ratings.
Style Transfer as Unsupervised Machine Translation
Language style transferring rephrases text with specific stylistic attributes while preserving the original attribute-independent content.
Ranked #3 on
Unsupervised Text Style Transfer
on GYAFC
R-VQA: Learning Visual Relation Facts with Semantic Attention for Visual Question Answering
To better utilize semantic knowledge in images, we propose a novel framework to learn visual relation facts for VQA.
Co-attending Free-form Regions and Detections with Multi-modal Multiplicative Feature Embedding for Visual Question Answering
Existing VQA methods mainly adopt the visual attention mechanism to associate the input question with corresponding image regions for effective question answering.
A Dirichlet Multinomial Mixture Model-based Approach for Short Text Clustering
Short text clustering has become an increasingly important task with the popularity of social media like Twitter, Google+, and Facebook.
