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Found 7 papers, 0 papers with code
When Large Language Models Meet Citation: A Survey
Such information could be incorporated into LLMs pre-training and improve the text representation in LLMs.
Learning to Select the Relevant History Turns in Conversational Question Answering
In this paper, we propose a framework, DHS-ConvQA (Dynamic History Selection in Conversational Question Answering), that first generates the context and question entities for all the history turns, which are then pruned on the basis of similarity they share in common with the question at hand.
Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering
However, these sequential questions are sometimes left implicit and thus require the resolution of some natural language phenomena such as anaphora and ellipsis.
GCN-based Multi-task Representation Learning for Anomaly Detection in Attributed Networks
Anomaly detection in attributed networks has received a considerable attention in recent years due to its applications in a wide range of domains such as finance, network security, and medicine.
A Survey on Participant Selection for Federated Learning in Mobile Networks
The main challenges of FL is that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID).
Conversational Question Answering: A Survey
Question answering (QA) systems provide a way of querying the information available in various formats including, but not limited to, unstructured and structured data in natural languages.
BERT-CoQAC: BERT-based Conversational Question Answering in Context
On one hand, we introduce a framework based on a publically available pre-trained language model called BERT for incorporating history turns into the system.
