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Found 9 papers, 6 papers with code
Instruct Once, Chat Consistently in Multiple Rounds: An Efficient Tuning Framework for Dialogue
Tuning pretrained language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents.
Mitigating Unhelpfulness in Emotional Support Conversations with Multifaceted AI Feedback
An emotional support conversation system aims to alleviate users' emotional distress and assist them in addressing their challenges.
How Far Are We from Believable AI Agents? A Framework for Evaluating the Believability of Human Behavior Simulation
Human behavior simulation of AI agents necessitates the agents to possess a quality of believability, which is crucial as it facilitates users in establishing trust toward the agents and streamlines the fulfillment of the agents' goal.
Self-Detoxifying Language Models via Toxification Reversal
Drawing on this idea, we devise a method to identify the toxification direction from the normal generation process to the one prompted with the negative prefix, and then steer the generation to the reversed direction by manipulating the information movement within the attention layers.
Aligning Language Models with Human Preferences via a Bayesian Approach
For this alignment, current popular methods leverage a reinforcement learning (RL) approach with a reward model trained on feedback from humans.
CARE: Causality Reasoning for Empathetic Responses by Conditional Graph Generation
Recent approaches to empathetic response generation incorporate emotion causalities to enhance comprehension of both the user's feelings and experiences.
Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning
Providing Emotional Support (ES) to soothe people in emotional distress is an essential capability in social interactions.
Modeling Content-Emotion Duality via Disentanglement for Empathetic Conversation
To solve the task, it is essential to model the content-emotion duality of a dialogue, which is composed of the content view (i. e., what personal experiences are described) and the emotion view (i. e., the feelings of the speaker on these experiences).
Empathetic Response Generation through Graph-based Multi-hop Reasoning on Emotional Causality
Empathetic response generation aims to comprehend the user emotion and then respond to it appropriately.
