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Found 10 papers, 4 papers with code
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Specifically, an adversarial LLM and a target LLM interplay with each other in an iterative manner, where the adversarial LLM aims to generate challenging prompts that elicit unsafe responses from the target LLM, while the target LLM is fine-tuned with safety aligned data on these adversarial prompts.
NormBank: A Knowledge Bank of Situational Social Norms
We present NormBank, a knowledge bank of 155k situational norms.
Modeling Motivational Interviewing Strategies On An Online Peer-to-Peer Counseling Platform
This work provides a deeper understanding of the use of motivational interviewing techniques on peer-to-peer counselor platforms and sheds light on how to build better training programs for volunteer counselors on online platforms.
COFFEE: Counterfactual Fairness for Personalized Text Generation in Explainable Recommendation
However, the bias inherent in user written text, often used for PTG model training, can inadvertently associate different levels of linguistic quality with users' protected attributes.
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
In this work, we introduce a new resource, not to authoritatively resolve moral ambiguities, but instead to facilitate systematic understanding of the intuitions, values and moral judgments reflected in the utterances of dialogue systems.
Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents
The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content.
Controllable Text Generation with Focused Variation
This work introduces Focused-Variation Network (FVN), a novel model to control language generation.
Plato Dialogue System: A Flexible Conversational AI Research Platform
Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
and their own objectives, and can only interact via natural language they generate.
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues.
