Search Results for author:
Found 12 papers, 5 papers with code
Give the Truth: Incorporate Semantic Slot into Abstractive Dialogue Summarization
Abstractive dialogue summarization suffers from a lots of factual errors, which are due to scattered salient elements in the multi-speaker information interaction process.
DivTOD: Unleashing the Power of LLMs for Diversifying Task-Oriented Dialogue Representations
Language models pre-trained on general text have achieved impressive results in diverse fields.
BootTOD: Bootstrap Task-oriented Dialogue Representations by Aligning Diverse Responses
Pre-trained language models have been successful in many scenarios.
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability.
What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning
We present deita (short for Data-Efficient Instruction Tuning for Alignment), a series of models fine-tuned from LLaMA and Mistral models using data samples automatically selected with our proposed approach.
Seen to Unseen: Exploring Compositional Generalization of Multi-Attribute Controllable Dialogue Generation
In this paper, we explore the compositional generalization for multi-attribute controllable dialogue generation where a model can learn from seen attribute values and generalize to unseen combinations.
FutureTOD: Teaching Future Knowledge to Pre-trained Language Model for Task-Oriented Dialogue
Pre-trained language models based on general text enable huge success in the NLP scenario.
Reinforcement Learning for Picking Cluttered General Objects with Dense Object Descriptors
We conduct experiments to demonstrate that our CODs is able to consistently represent seen and unseen cluttered objects, which allowed for the picking policy to robustly pick cluttered general objects.
Learning Sim-to-Real Dense Object Descriptors for Robotic Manipulation
In this paper, we present Sim-to-Real Dense Object Nets (SRDONs), a dense object descriptor that not only understands the object via appropriate representation but also maps simulated and real data to a unified feature space with pixel consistency.
Semi-Supervised Knowledge-Grounded Pre-training for Task-Oriented Dialog Systems
Recent advances in neural approaches greatly improve task-oriented dialogue (TOD) systems which assist users to accomplish their goals.
Domain-Oriented Prefix-Tuning: Towards Efficient and Generalizable Fine-tuning for Zero-Shot Dialogue Summarization
The most advanced abstractive dialogue summarizers lack generalization ability on new domains and the existing researches for domain adaptation in summarization generally rely on large-scale pre-trainings.
TODSum: Task-Oriented Dialogue Summarization with State Tracking
Previous dialogue summarization datasets mainly focus on open-domain chitchat dialogues, while summarization datasets for the broadly used task-oriented dialogue haven't been explored yet.
