Search Results for author:
Found 29 papers, 12 papers with code
Show, Don’t Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.
Robust Multi-Objective Preference Alignment with Online DPO
Multi-objective preference alignment of large language models (LLMs) is critical for developing AI systems that are more configurable, personalizable, helpful, and safe.
MoDE: Effective Multi-task Parameter Efficient Fine-Tuning with a Mixture of Dyadic Experts
Parameter-efficient fine-tuning techniques like Low-Rank Adaptation (LoRA) have revolutionized the adaptation of large language models (LLMs) to diverse tasks.
Improve Mathematical Reasoning in Language Models by Automated Process Supervision
To date, the methods used to collect process supervision data have relied on either human annotation or per-step Monte Carlo estimation, both prohibitively expensive to scale, thus hindering the broad application of this technique.
Parameter Efficient Reinforcement Learning from Human Feedback
In this work, we empirically evaluate the setup of Parameter Efficient Reinforcement Learning from Human Feedback (PE-RLHF) that leverages LoRA fine-tuning for Reward Modeling, and Reinforcement Learning.
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but gathering high-quality preference labels is expensive.
Conversational Recommendation as Retrieval: A Simple, Strong Baseline
Conversational recommendation systems (CRS) aim to recommend suitable items to users through natural language conversation.
AnyTOD: A Programmable Task-Oriented Dialog System
We propose AnyTOD, an end-to-end, zero-shot task-oriented dialog (TOD) system capable of handling unseen tasks without task-specific training.
Speech Aware Dialog System Technology Challenge (DSTC11)
The research on this topic is stymied by the lack of a public corpus.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.
Show, Don't Tell: Demonstrations Outperform Descriptions for Schema-Guided Task-Oriented Dialogue
Building universal dialogue systems that operate across multiple domains/APIs and generalize to new ones with minimal overhead is a critical challenge.
A Unified Approach to Entity-Centric Context Tracking in Social Conversations
Second, we open-source a neural network architecture for context tracking.
Description-Driven Task-Oriented Dialog Modeling
Task-oriented dialogue (TOD) systems are required to identify key information from conversations for the completion of given tasks.
SGD-X: A Benchmark for Robust Generalization in Schema-Guided Dialogue Systems
Zero/few-shot transfer to unseen services is a critical challenge in task-oriented dialogue research.
UIBert: Learning Generic Multimodal Representations for UI Understanding
Our key intuition is that the heterogeneous features in a UI are self-aligned, i. e., the image and text features of UI components, are predictive of each other.
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines
We also benchmark a few state of the art dialogue state tracking models on the corrected dataset to facilitate comparison for future work.
Text-to-Text Pre-Training for Data-to-Text Tasks
We study the pre-train + fine-tune strategy for data-to-text tasks.
Ranked #1 on
Data-to-Text Generation
on ToTTo
Template Guided Text Generation for Task-Oriented Dialogue
Virtual assistants such as Google Assistant, Amazon Alexa, and Apple Siri enable users to interact with a large number of services and APIs on the web using natural language.
Ranked #2 on
Data-to-Text Generation
on MULTIWOZ 2.1
Schema-Guided Dialogue State Tracking Task at DSTC8
The goal of this task is to develop dialogue state tracking models suitable for large-scale virtual assistants, with a focus on data-efficient joint modeling across domains and zero-shot generalization to new APIs.
The Eighth Dialog System Technology Challenge
This paper introduces the Eighth Dialog System Technology Challenge.
Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset
In this work, we introduce the the Schema-Guided Dialogue (SGD) dataset, containing over 16k multi-domain conversations spanning 16 domains.
DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks
Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation.
Learning Question-Guided Video Representation for Multi-Turn Video Question Answering
Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans.
Multi-task learning for Joint Language Understanding and Dialogue State Tracking
This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems.
An Efficient Approach to Encoding Context for Spoken Language Understanding
In task-oriented dialogue systems, spoken language understanding, or SLU, refers to the task of parsing natural language user utterances into semantic frames.
Building a Conversational Agent Overnight with Dialogue Self-Play
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains.
Scalable Multi-Domain Dialogue State Tracking
We introduce a novel framework for state tracking which is independent of the slot value set, and represent the dialogue state as a distribution over a set of values of interest (candidate set) derived from the dialogue history or knowledge.
Dialogue State Tracking
Multi-domain Dialogue State Tracking
+2
A Fast Unified Model for Parsing and Sentence Understanding
Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences.
Ranked #86 on
Natural Language Inference
on SNLI
