Search Results for author:
Found 20 papers, 7 papers with code
ChatASU: Evoking LLM's Reflexion to Truly Understand Aspect Sentiment in Dialogues
Specifically, this TSA treats the ACR task as an auxiliary task to boost the performance of the primary ASU task, and further integrates trusted learning into reflexion mechanisms to alleviate the LLMs-intrinsic factual hallucination problem in TSA.
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG)
In this work, we conduct extensive empirical studies with novel attack methods, which demonstrate the vulnerability of RAG systems on leaking the private retrieval database.
Self-supervised Heterogeneous Graph Variational Autoencoders
Instead of directly reconstructing raw features for attributed nodes, SHAVA generates the initial low-dimensional representation matrix for all the nodes, based on which raw features of attributed nodes are further reconstructed to leverage accurate attributes.
Exploiting Latent Attribute Interaction with Transformer on Heterogeneous Information Networks
In addition, most of them only consider the interactions between nodes while neglecting the high-order information behind the latent interactions among different node features.
MILL: Mutual Verification with Large Language Models for Zero-Shot Query Expansion
While existing methods expand queries using retrieved or generated contextual documents, each approach has notable limitations.
Enhancing Graph Neural Networks with Structure-Based Prompt
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.
Exploring Memorization in Fine-tuned Language Models
In this work, we conduct the first comprehensive analysis to explore language models' (LMs) memorization during fine-tuning across tasks.
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback
Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR).
I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
Moreover, the proposed implicit interaction is compatible with special pre-training and knowledge distillation for passage retrieval, which brings a new state-of-the-art performance.
Pretrained Language Model based Web Search Ranking: From Relevance to Satisfaction
In this work, we focus on ranking user satisfaction rather than relevance in web search, and propose a PLM-based framework, namely SAT-Ranker, which comprehensively models different dimensions of user satisfaction in a unified manner.
Layout-aware Webpage Quality Assessment
To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement Learning
Detecting anomalous trajectories has become an important task in many location-based applications.
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval
Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.
Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
To leverage a reliable knowledge, we propose a novel knowledge graph distillation method and obtain a knowledge meta graph as the bridge between query and passage.
Pre-trained Language Model for Web-scale Retrieval in Baidu Search
More importantly, we present a practical system workflow for deploying the model in web-scale retrieval.
Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation
Based on it, a more robust doubly robust (MRDR) estimator has been proposed to further reduce its variance while retaining its double robustness.
Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning
Similar trajectory search is a fundamental problem and has been well studied over the past two decades.
Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction
Specifically, our proposed model is designed as a spatial-temporal network with a meta-learning paradigm.
Affinity Derivation and Graph Merge for Instance Segmentation
We present an instance segmentation scheme based on pixel affinity information, which is the relationship of two pixels belonging to a same instance.
Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
Our proposed approach hinges upon the key intuition that the decision making process (in groups) is generally dynamic, i. e., a user's decision is highly dependent on the other group members.
