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Found 34 papers, 11 papers with code
SafetyKit: First Aid for Measuring Safety in Open-domain Conversational Systems
We then empirically assess the extent to which current tools can measure these effects and current systems display them.
Guiding the Release of Safer E2E Conversational AI through Value Sensitive Design
Over the last several years, end-to-end neural conversational agents have vastly improved their ability to carry unrestricted, open-domain conversations with humans.
Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts
Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format.
Improving Open Language Models by Learning from Organic Interactions
We present BlenderBot 3x, an update on the conversational model BlenderBot 3, which is now trained using organic conversation and feedback data from participating users of the system in order to improve both its skills and safety.
The HCI Aspects of Public Deployment of Research Chatbots: A User Study, Design Recommendations, and Open Challenges
Publicly deploying research chatbots is a nuanced topic involving necessary risk-benefit analyses.
BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage
We present BlenderBot 3, a 175B parameter dialogue model capable of open-domain conversation with access to the internet and a long-term memory, and having been trained on a large number of user defined tasks.
Learning New Skills after Deployment: Improving open-domain internet-driven dialogue with human feedback
We then study various algorithms for improving from such feedback, including standard supervised learning, rejection sampling, model-guiding and reward-based learning, in order to make recommendations on which type of feedback and algorithms work best.
Learning from data in the mixed adversarial non-adversarial case: Finding the helpers and ignoring the trolls
The promise of interaction between intelligent conversational agents and humans is that models can learn from such feedback in order to improve.
Human Evaluation of Conversations is an Open Problem: comparing the sensitivity of various methods for evaluating dialogue agents
At the heart of improving conversational AI is the open problem of how to evaluate conversations.
SaFeRDialogues: Taking Feedback Gracefully after Conversational Safety Failures
Current open-domain conversational models can easily be made to talk in inadequate ways.
Detecting Inspiring Content on Social Media
We release a dataset of 5, 800 inspiring and 5, 800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.
Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling
Over the last several years, end-to-end neural conversational agents have vastly improved in their ability to carry a chit-chat conversation with humans.
Bot-Adversarial Dialogue for Safe Conversational Agents
Conversational agents trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior.
Reducing conversational agents' overconfidence through linguistic calibration
While improving neural dialogue agents' factual accuracy is the object of much research, another important aspect of communication, less studied in the setting of neural dialogue, is transparency about ignorance.
Recipes for Safety in Open-domain Chatbots
Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases.
Controlling Style in Generated Dialogue
Open-domain conversation models have become good at generating natural-sounding dialogue, using very large architectures with billions of trainable parameters.
Open-Domain Conversational Agents: Current Progress, Open Problems, and Future Directions
We present our view of what is necessary to build an engaging open-domain conversational agent: covering the qualities of such an agent, the pieces of the puzzle that have been built so far, and the gaping holes we have not filled yet.
Multi-scale Transformer Language Models
We investigate multi-scale transformer language models that learn representations of text at multiple scales, and present three different architectures that have an inductive bias to handle the hierarchical nature of language.
Recipes for building an open-domain chatbot
Building open-domain chatbots is a challenging area for machine learning research.
Can You Put it All Together: Evaluating Conversational Agents' Ability to Blend Skills
Being engaging, knowledgeable, and empathetic are all desirable general qualities in a conversational agent.
All-in-One Image-Grounded Conversational Agents
As single-task accuracy on individual language and image tasks has improved substantially in the last few years, the long-term goal of a generally skilled agent that can both see and talk becomes more feasible to explore.
Zero-Shot Fine-Grained Style Transfer: Leveraging Distributed Continuous Style Representations to Transfer To Unseen Styles
Text style transfer is usually performed using attributes that can take a handful of discrete values (e. g., positive to negative reviews).
Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training
Generative dialogue models currently suffer from a number of problems which standard maximum likelihood training does not address.
The Dialogue Dodecathlon: Open-Domain Knowledge and Image Grounded Conversational Agents
We introduce dodecaDialogue: a set of 12 tasks that measures if a conversational agent can communicate engagingly with personality and empathy, ask questions, answer questions by utilizing knowledge resources, discuss topics and situations, and perceive and converse about images.
Revisiting the Evaluation of Theory of Mind through Question Answering
Theory of mind, i. e., the ability to reason about intents and beliefs of agents is an important task in artificial intelligence and central to resolving ambiguous references in natural language dialogue.
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
These issues can be alleviated by treating recommendation as an interactive dialogue task instead, where an expert recommender can sequentially ask about someone's preferences, react to their requests, and recommend more appropriate items.
I Know the Feeling: Learning to Converse with Empathy
Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling.
Multiple-Attribute Text Rewriting
The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style".
On the Pitfalls of Measuring Emergent Communication
How do we know if communication is emerging in a multi-agent system?
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill.
Multiple-Attribute Text Style Transfer
The dominant approach to unsupervised "style transfer" in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style".
Learning End-to-End Goal-Oriented Dialog
We show similar result patterns on data extracted from an online concierge service.
Learning Convolutional Feature Hierarchies for Visual Recognition
We propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features.
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the raw input.
