Search Results for author:
Found 15 papers, 2 papers with code
Learn to Disguise: Avoid Refusal Responses in LLM's Defense via a Multi-agent Attacker-Disguiser Game
In this paper, we propose a multi-agent attacker-disguiser game approach to achieve a weak defense mechanism that allows the large model to both safely reply to the attacker and hide the defense intent.
LLM-based Privacy Data Augmentation Guided by Knowledge Distillation with a Distribution Tutor for Medical Text Classification
As sufficient data are not always publically accessible for model training, researchers exploit limited data with advanced learning algorithms or expand the dataset via data augmentation (DA).
Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation
Building models of natural language processing (NLP) is challenging in low-resource scenarios where only limited data are available.
Trivial bundle embeddings for learning graph representations
Embedding scale-free networks to hyperbolic spaces offer an exciting alternative but incurs distortions when embedding assortative networks with latent geometries not hyperbolic.
Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks
The task requires models to generate personalized responses for a speaker given a few conversations from the speaker and a social network.
When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
Model-Agnostic Meta-Learning (MAML), a model-agnostic meta-learning method, is successfully employed in NLP applications including few-shot text classification and multi-domain low-resource language generation.
Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory.
Multi-task Learning via Adaptation to Similar Tasks for Mortality Prediction of Diverse Rare Diseases
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare.
Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
Training the generative models with minimal corpus is one of the critical challenges for building open-domain dialogue systems.
An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems.
Human-computer conversation systems have attracted much attention in Natural Language Processing.
Diversifying Neural Conversation Model with Maximal Marginal Relevance
However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies.
How to Make Context More Useful? An Empirical Study on Context-Aware Neural Conversational Models
Generative conversational systems are attracting increasing attention in natural language processing (NLP).
Two are Better than One: An Ensemble of Retrieval- and Generation-Based Dialog Systems
In this paper, we propose a novel ensemble of retrieval-based and generation-based dialog systems in the open domain.
Dialogue Session Segmentation by Embedding-Enhanced TextTiling
In human-computer conversation systems, the context of a user-issued utterance is particularly important because it provides useful background information of the conversation.
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
Using neural networks to generate replies in human-computer dialogue systems is attracting increasing attention over the past few years.
