Search Results for author: Jiazhan Feng

Found 12 papers, 6 papers with code

Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations

no code implementations COLING 2022 Jiazhan Feng, Chongyang Tao, Zhen Li, Chang Liu, Tao Shen, Dongyan Zhao

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.

Retrieval

A Step Closer to Comprehensive Answers: Constrained Multi-Stage Question Decomposition with Large Language Models

1 code implementation13 Nov 2023 Hejing Cao, Zhenwei An, Jiazhan Feng, Kun Xu, Liwei Chen, Dongyan Zhao

While large language models exhibit remarkable performance in the Question Answering task, they are susceptible to hallucinations.

Question Answering

Language Models can be Logical Solvers

no code implementations10 Nov 2023 Jiazhan Feng, Ruochen Xu, Junheng Hao, Hiteshi Sharma, Yelong Shen, Dongyan Zhao, Weizhu Chen

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.

Decision Making Language Modelling +1

Teaching Text-to-Image Models to Communicate in Dialog

no code implementations27 Sep 2023 Xiaowen Sun, Jiazhan Feng, Yuxuan Wang, Yuxuan Lai, Xingyu Shen, Dongyan Zhao

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.

Sentence Text-to-Image Generation

Synergistic Interplay between Search and Large Language Models for Information Retrieval

1 code implementation12 May 2023 Jiazhan Feng, Chongyang Tao, Xiubo Geng, Tao Shen, Can Xu, Guodong Long, Dongyan Zhao, Daxin Jiang

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).

Information Retrieval Retrieval

WizardLM: Empowering Large Language Models to Follow Complex Instructions

4 code implementations24 Apr 2023 Can Xu, Qingfeng Sun, Kai Zheng, Xiubo Geng, Pu Zhao, Jiazhan Feng, Chongyang Tao, Daxin Jiang

In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans.

Instruction Following

Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning

no code implementations1 Oct 2021 Chongyang Tao, Jiazhan Feng, Chang Liu, Juntao Li, Xiubo Geng, Daxin Jiang

For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks.

Re-Ranking Retrieval

Response Ranking with Multi-types of Deep Interactive Representations in Retrieval-based Dialogues

1 code implementation ACM Transactions on Information Systems 2021 Ruijian Xu, Chongyang Tao, Jiazhan Feng, Wei Wu, Rui Yan, Dongyan Zhao

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.

Conversational Response Selection Retrieval

A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations

no code implementations ACL 2021 Chongyang Tao, Changyu Chen, Jiazhan Feng, Ji-Rong Wen, Rui Yan

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.

Language Modelling Retrieval +1

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