Search Results for author:
Found 33 papers, 14 papers with code
Towards Socially Intelligent Agents with Mental State Transition and Human Value
One of which is to track the agent’s mental state transition and teach the agent to make decisions guided by its value like a human.
CultureBank: An Online Community-Driven Knowledge Base Towards Culturally Aware Language Technologies
To enhance language models' cultural awareness, we design a generalizable pipeline to construct cultural knowledge bases from different online communities on a massive scale.
A Safe Harbor for AI Evaluation and Red Teaming
Independent evaluation and red teaming are critical for identifying the risks posed by generative AI systems.
Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future
As Natural Language Processing (NLP) systems become increasingly integrated into human social life, these technologies will need to increasingly rely on social intelligence.
The Mirrored Influence Hypothesis: Efficient Data Influence Estimation by Harnessing Forward Passes
Inspired by this, we introduce a new method for estimating the influence of training data, which requires calculating gradients for specific test samples, paired with a forward pass for each training point.
How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs
This paper introduces a new perspective to jailbreak LLMs as human-like communicators, to explore this overlooked intersection between everyday language interaction and AI safety.
The Earth is Flat because...: Investigating LLMs' Belief towards Misinformation via Persuasive Conversation
Therefore, in this study, we delve into LLMs' susceptibility to persuasive conversations, particularly on factual questions that they can answer correctly.
Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach
Using COVID-19 as a testbed domain, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach.
Controllable Mixed-Initiative Dialogue Generation through Prompting
The standard approach has been fine-tuning pre-trained language models to perform generation conditioned on these intents.
AutoReply: Detecting Nonsense in Dialogue Introspectively with Discriminative Replies
Existing approaches built separate classifiers to detect nonsense in dialogues.
Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge.
When Life Gives You Lemons, Make Cherryade: Converting Feedback from Bad Responses into Good Labels
Deployed dialogue agents have the potential to integrate human feedback to continuously improve themselves.
Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks
Dialogue systems capable of social influence such as persuasion, negotiation, and therapy, are essential for extending the use of technology to numerous realistic scenarios.
Just Fine-tune Twice: Selective Differential Privacy for Large Language Models
Utilizing the fact that sensitive information in language data tends to be sparse, Shi et al. (2021) formalized a DP notion extension called Selective Differential Privacy (SDP) to protect only the sensitive tokens defined by a policy function.
Seamlessly Integrating Factual Information and Social Content with Persuasive Dialogue
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 to the topic.
Selective Differential Privacy for Language Modeling
Given that the private information in natural language is sparse (for example, the bulk of an email might not carry personally identifiable information), we propose a new privacy notion, selective differential privacy, to provide rigorous privacy guarantees on the sensitive portion of the data to improve model utility.
PRAL: A Tailored Pre-Training Model for Task-Oriented Dialog Generation
However, the performance of pre-trained models on task-oriented dialog tasks is still under-explored.
Dialoging Resonance: How Users Perceive, Reciprocate and React to Chatbot's Self-Disclosure in Conversational Recommendations
Using chatbots to deliver recommendations is increasingly popular.
LEGOEval: An Open-Source Toolkit for Dialogue System Evaluation via Crowdsourcing
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 Mechanical Turk.
DEUX: An Attribute-Guided Framework for Sociable Recommendation Dialog Systems
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.
Towards Socially Intelligent Agents with Mental State Transition and Human Utility
One of which is to track the agent's mental state transition and teach the agent to make decisions guided by its value like a human.
Refine and Imitate: Reducing Repetition and Inconsistency in Dialogue Generation via Reinforcement Learning and Human Demonstration
Despite the recent success of large-scale language models on various downstream NLP tasks, the repetition and inconsistency problems still persist in dialogue response generation.
Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstration
Persuasion dialogue systems reflect the machine's ability to make strategic moves beyond verbal communication, and therefore differentiate themselves from task-oriented or open-domain dialogue systems and have their own unique values.
Understanding User Resistance Strategies in Persuasive Conversations
Furthermore, we analyze the relationships between persuasion strategies and persuasion resistance strategies.
INSPIRED: Toward Sociable Recommendation Dialog Systems
To better understand how humans make recommendations in communication, we design an annotation scheme related to recommendation strategies based on social science theories and annotate these dialogs.
Structured Attention for Unsupervised Dialogue Structure Induction
Inducing a meaningful structural representation from one or a set of dialogues is a crucial but challenging task in computational linguistics.
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
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 tasks.
End-to-End Trainable Non-Collaborative Dialog System
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.
How to Build User Simulators to Train RL-based Dialog Systems
We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly.
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
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 dialogue systems.
Unsupervised Dialog Structure Learning
The learned dialog structure can shed light on how to analyze human dialogs, and more importantly contribute to the design and evaluation of dialog systems.
Exploiting Common Characters in Chinese and Japanese to Learn Cross-Lingual Word Embeddings via Matrix Factorization
Learning vector space representation of words (i. e., word embeddings) has recently attracted wide research interests, and has been extended to cross-lingual scenario.
Sentiment Adaptive End-to-End Dialog Systems
End-to-end learning framework is useful for building dialog systems for its simplicity in training and efficiency in model updating.
