Search Results for author:
Found 21 papers, 5 papers with code
GAP: Graph-Assisted Prompts for Dialogue-based Medication Recommendation
Medication recommendations have become an important task in the healthcare domain, especially in measuring the accuracy and safety of medical dialogue systems (MDS).
A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future
Reward Model (RM) has demonstrated impressive potential for enhancing Large Language Models (LLM), as RM can serve as a proxy for human preferences, providing signals to guide LLMs' behavior in various tasks.
Exploiting Prefix-Tree in Structured Output Interfaces for Enhancing Jailbreak Attacking
We investigate LLMs' safety mechanisms and their recent applications, revealing a new threat model targeting structured output interfaces, which enable attackers to manipulate the inner logit during LLM generation, requiring only API access permissions.
DySpec: Faster Speculative Decoding with Dynamic Token Tree Structure
Under low temperature setting, DySpec can improve the throughput up to 9. 1$\times$ and reduce the latency up to 9. 4$\times$ on Llama2-70B.
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG.
MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph
Multi-modal knowledge graphs have emerged as a powerful approach for information representation, combining data from different modalities such as text, images, and videos.
CombAlign: Enhancing Model Expressiveness in Unsupervised Graph Alignment
We investigate the model expressiveness from two aspects.
Leveraging Large Language Model as Simulated Patients for Clinical Education
Simulated Patients (SPs) play a crucial role in clinical medical education by providing realistic scenarios for student practice.
LLMaAA: Making Large Language Models as Active Annotators
Recently, the superior few-shot performance of large language models (LLMs) has propelled the development of dataset generation, where the training data are solely synthesized from LLMs.
Two is Better Than One: Answering Complex Questions by Multiple Knowledge Sources with Generalized Links
Incorporating multiple knowledge sources is proven to be beneficial for answering complex factoid questions.
ADMUS: A Progressive Question Answering Framework Adaptable to Multiple Knowledge Sources
With the introduction of deep learning models, semantic parsingbased knowledge base question answering (KBQA) systems have achieved high performance in handling complex questions.
TopoBERT: Plug and Play Toponym Recognition Module Harnessing Fine-tuned BERT
Extracting precise geographical information from textual contents is crucial in a plethora of applications.
VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph
Semantic Web technology has successfully facilitated many RDF models with rich data representation methods.
gBuilder: A Scalable Knowledge Graph Construction System for Unstructured Corpus
Furthermore, we also design a cloud-based self-adaptive task scheduling for gBuilder to ensure its scalability on large-scale knowledge graph construction.
Crake: Causal-Enhanced Table-Filler for Question Answering over Large Scale Knowledge Base
Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect and connect related nodes in a query.
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering
We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question.
Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases
The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
A State-transition Framework to Answer Complex Questions over Knowledge Base
Although natural language question answering over knowledge graphs have been studied in the literature, existing methods have some limitations in answering complex questions.
