Search Results for author:
Found 12 papers, 6 papers with code
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations
In this paper, we propose a reciprocal learning approach to jointly optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels.
Multi-Granularity Structural Knowledge Distillation for Language Model Compression
Transferring the knowledge to a small model through distillation has raised great interest in recent years.
A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models
While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations.
Language Models can be Logical Solvers
Despite their impressive performance, any parsing errors will inevitably result in the failure of the execution of the external logical solver and no answer to the logical questions.
Teaching Text-to-Image Models to Communicate in Dialog
In this paper, we focus on the innovative dialog-to-image generation task, where the model synthesizes a high-resolution image aligned with the given dialog context as a response.
Synergistic Interplay between Search and Large Language Models for Information Retrieval
Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs).
WizardLM: Empowering Large Language Models to Follow Complex Instructions
In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
First, it is the largest multi-modal conversation dataset by the number of dialogues by 88x.
Ranked #2 on
Multimodal Intent Recognition
on MMDialog
Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning
For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.
Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues
To tackle these challenges, we propose a representation[K]-interaction[L]-matching framework that explores multiple types of deep interactive representations to build context-response matching models for response selection.
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations
Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e. g., documents) when conversing with humans.
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems
Under the framework, we simultaneously learn two matching models with independent training sets.
