activity
20202025
most citedMAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

8 citations · 21 across the 18 of their papers we have counts for

collaborators
Showing 2023Show all

8 papers · 1 filter

cs.CL2023★ 8 cited

MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL

Bing Wang, Changyu Ren, Jian Yang +8

Recent LLM-based Text-to-SQL methods usually suffer from significant performance degradation on "huge" databases and complex user questions that require multi-step reasoning. Moreo…

cs.CL2023★ 1 cited

Multi-Stage Pre-training Enhanced by ChatGPT for Multi-Scenario Multi-Domain Dialogue Summarization

Weixiao Zhou, Gengyao Li, Xianfu Cheng +4

Dialogue summarization involves a wide range of scenarios and domains. However, existing methods generally only apply to specific scenarios or domains. In this study, we propose a…

cs.CL2023

KnowPrefix-Tuning: A Two-Stage Prefix-Tuning Framework for Knowledge-Grounded Dialogue Generation

Jiaqi Bai, Zhao Yan, Jian Yang +3

Existing knowledge-grounded conversation systems generate responses typically in a retrieve-then-generate manner. They require a large knowledge base and a strong knowledge retriev…

cs.CL2023★ 1 cited

GripRank: Bridging the Gap between Retrieval and Generation via the Generative Knowledge Improved Passage Ranking

Jiaqi Bai, Hongcheng Guo, Jiaheng Liu +4

Retrieval-enhanced text generation has shown remarkable progress on knowledge-intensive language tasks, such as open-domain question answering and knowledge-enhanced dialogue gener…

cs.CL2023

Retrieval-Augmented Classification with Decoupled Representation

Xinnian Liang, Shuangzhi Wu, Hui Huang +3

Retrieval augmented methods have shown promising results in various classification tasks. However, existing methods focus on retrieving extra context to enrich the input, which is…

cs.CL2023★ 4 cited

SCM: Enhancing Large Language Model with Self-Controlled Memory Framework

Bing Wang, Xinnian Liang, Jian Yang +6

Large Language Models (LLMs) are constrained by their inability to process lengthy inputs, resulting in the loss of critical historical information. To address this limitation, in…

cs.CL2023★ 2 cited

Character, Word, or Both? Revisiting the Segmentation Granularity for Chinese Pre-trained Language Models

Xinnian Liang, Zefan Zhou, Hui Huang +5

Pretrained language models (PLMs) have shown marvelous improvements across various NLP tasks. Most Chinese PLMs simply treat an input text as a sequence of characters, and complete…

cs.CL2023

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Xinnian Liang, Shuangzhi Wu, Chenhao Cui +3

Dialogue summarization aims to condense a given dialogue into a simple and focused summary text. Typically, both the roles' viewpoints and conversational topics change in the dialo…