90 citations · 230 across the 22 of their papers we have counts for
22 papers
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes
Myeongseob Ko, Feiyang Kang, Weiyan Shi +3
Large-scale black-box models have become ubiquitous across numerous applications. Understanding the influence of individual training data sources on predictions made by these model…
Controllable Mixed-Initiative Dialogue Generation through Prompting
Maximillian Chen, Xiao Yu, Weiyan Shi +2
Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particu…
AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies
Weiyan Shi, Emily Dinan, Adi Renduchintala +4
Existing approaches built separate classifiers to detect nonsense in dialogues. In this paper, we show that without external classifiers, dialogue models can detect errors in their…
When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels
Weiyan Shi, Emily Dinan, Kurt Shuster +2
Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves. However, humans may not always provide explicit signals when the chatbot…
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks
Kushal Chawla, Weiyan Shi, Jingwen Zhang +3
Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios. Howeve…
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Weiyan Shi, Ryan Shea, Si Chen +3
Protecting large language models from privacy leakage is becoming increasingly crucial with their wide adoption in real-world products. Yet applying differential privacy (DP), a ca…
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
Maximillian Chen, Weiyan Shi, Feifan Yan +4
Complex conversation settings such as persuasion involve communicating changes in attitude or behavior, so users' perspectives need to be addressed, even when not directly related…
Selective Differential Privacy for Language Modeling
Weiyan Shi, Aiqi Cui, Evan Li +2
With the increasing applications of language models, it has become crucial to protect these models from leaking private information. Previous work has attempted to tackle this chal…
Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot's Self-Disclosure in Conversational Recommendations
Kai-Hui Liang, Weiyan Shi, Yoojung Oh +3
Using chatbots to deliver recommendations is increasingly popular. The design of recommendation chatbots has primarily been taking an information-centric approach by focusing on th…
LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing
Yu Li, Josh Arnold, Feifan Yan +2
We present LEGOEval, an open-source toolkit that enables researchers to easily evaluate dialogue systems in a few lines of code using the online crowdsource platform, Amazon Mechan…
DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems
Yu Li, Shirley Anugrah Hayati, Weiyan Shi +1
It is important for sociable recommendation dialog systems to perform as both on-task content and social content to engage users and gain their favor. In addition to understand the…
Towards Socially Intelligent Agents with Mental State Transition and Human Utility
Liang Qiu, Yizhou Zhao, Yuan Liang +4
Building a socially intelligent agent involves many challenges. One of which is to track the agent's mental state transition and teach the agent to make decisions guided by its val…
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration
Weiyan Shi, Yu Li, Saurav Sahay +1
Persuasion dialogue systems reflect the machine's ability to make strategic moves beyond verbal communication, and therefore differentiate themselves from task-oriented or open-dom…
Structured Attention for Unsupervised Dialogue Structure Induction
Liang Qiu, Yizhou Zhao, Weiyan Shi +5
Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics. Advancement made in this area is crit…
INSPIRED: Toward Sociable Recommendation Dialog Systems
Shirley Anugrah Hayati, Dongyeop Kang, Qingxiaoyang Zhu +2
In recommendation dialogs, humans commonly disclose their preference and make recommendations in a friendly manner. However, this is a challenge when developing a sociable recommen…
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
Jing Gu, Qingyang Wu, Chongruo Wu +2
The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation task…
Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
Weiyan Shi, Xuewei Wang, Yoo Jung Oh +3
Intelligent conversational agents, or chatbots, can take on various identities and are increasingly engaging in more human-centered conversations with persuasive goals. However, li…
End-to-End Trainable Non-Collaborative Dialog System
Yu Li, Kun Qian, Weiyan Shi +1
End-to-end task-oriented dialog models have achieved promising performance on collaborative tasks where users willingly coordinate with the system to complete a given task. While i…
How to Build User Simulators to Train RL-based Dialog Systems
Weiyan Shi, Kun Qian, Xuewei Wang +1
User simulators are essential for training reinforcement learning (RL) based dialog models. The performance of the simulator directly impacts the RL policy. However, building a goo…
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Xuewei Wang, Weiyan Shi, Richard Kim +4
Developing intelligent persuasive conversational agents to change people's opinions and actions for social good is the frontier in advancing the ethical development of automated di…
Unsupervised Dialog Structure Learning
Weiyan Shi, Tiancheng Zhao, Zhou Yu
Learning a shared dialog structure from a set of task-oriented dialogs is an important challenge in computational linguistics. The learned dialog structure can shed light on how to…
Sentiment Adaptive End-to-End Dialog Systems
Weiyan Shi, Zhou Yu
End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating. However, current end-to-end approaches only con…