Search Results for author: Binhua Li

Found 17 papers, 9 papers with code

Unifying Structured Data as Graph for Data-to-Text Pre-Training

1 code implementation2 Jan 2024 Shujie Li, Liang Li, Ruiying Geng, Min Yang, Binhua Li, Guanghu Yuan, Wanwei He, Shao Yuan, Can Ma, Fei Huang, Yongbin Li

In this paper, we unify different types of structured data (i. e., table, key-value data, knowledge graph) into the graph format and cast different data-to-text generation tasks as graph-to-text generation.

Data-to-Text Generation

CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality

no code implementations20 Jun 2023 Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li

To alleviate these limitations, in this paper, we present CATS, a pragmatic Chinese answer-to-sequence dataset with large scale and high quality.

Iterative Forward Tuning Boosts In-context Learning in Language Models

no code implementations22 May 2023 Jiaxi Yang, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li

In this paper, we propose an effective and efficient two-stage framework to boost ICL in LLMs by exploiting a dual form between Transformer attention and gradient descent-based optimization.

Decision Making In-Context Learning +1

Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs

no code implementations NeurIPS 2023 Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Rongyu Cao, Ruiying Geng, Nan Huo, Xuanhe Zhou, Chenhao Ma, Guoliang Li, Kevin C. C. Chang, Fei Huang, Reynold Cheng, Yongbin Li

Our emphasis on database values highlights the new challenges of dirty database contents, external knowledge between NL questions and database contents, and SQL efficiency, particularly in the context of massive databases.

Semantic Parsing SQL Parsing +1

Plan-then-Seam: Towards Efficient Table-to-Text Generation

1 code implementation10 Feb 2023 Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Binhua Li, Yongbin Li

Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables.

Table-to-Text Generation

Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

1 code implementation31 Jan 2023 Yunhu Ye, Binyuan Hui, Min Yang, Binhua Li, Fei Huang, Yongbin Li

To alleviate the above challenges, we exploit large language models (LLMs) as decomposers for effective table-based reasoning, which (i) decompose huge evidence (a huge table) into sub-evidence (a small table) to mitigate the interference of useless information for table reasoning; and (ii) decompose complex questions into simpler sub-questions for text reasoning.

Hallucination Semantic Parsing +1

Towards Generalizable and Robust Text-to-SQL Parsing

1 code implementation23 Oct 2022 Chang Gao, Bowen Li, Wenxuan Zhang, Wai Lam, Binhua Li, Fei Huang, Luo Si, Yongbin Li

Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.

SQL Parsing Text-To-SQL

STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing

1 code implementation21 Oct 2022 ZeFeng Cai, Xiangyu Li, Binyuan Hui, Min Yang, Bowen Li, Binhua Li, Zheng Cao, Weijie Li, Fei Huang, Luo Si, Yongbin Li

Concretely, we propose two novel pre-training objectives which respectively explore the context-dependent interactions of NL utterances and SQL queries within each text-to-SQL conversation: (i) schema state tracking (SST) objective that tracks and explores the schema states of context-dependent SQL queries in the form of schema-states by predicting and updating the value of each schema slot during interaction; (ii) utterance dependency tracking (UDT) objective that employs weighted contrastive learning to pull together two semantically similar NL utterances and push away the representations of semantically dissimilar NL utterances within each conversation.

Contrastive Learning SQL Parsing +1

SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers

1 code implementation COLING 2022 Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li

To improve the generalizability and stability of neural text-to-SQL parsers, we propose a model uncertainty constraint to refine the query representations by enforcing the output representations of different perturbed encoding networks to be consistent with each other.

SQL Parsing Text-To-SQL

A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions

no code implementations29 Aug 2022 Bowen Qin, Binyuan Hui, Lihan Wang, Min Yang, Jinyang Li, Binhua Li, Ruiying Geng, Rongyu Cao, Jian Sun, Luo Si, Fei Huang, Yongbin Li

In recent years, deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output SQL query.

SQL Parsing Text-To-SQL

Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing

2 code implementations28 Jun 2022 Lihan Wang, Bowen Qin, Binyuan Hui, Bowen Li, Min Yang, Bailin Wang, Binhua Li, Fei Huang, Luo Si, Yongbin Li

The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i. e., properly recognizing mentions of unseen columns or tables when generating SQLs.

SQL Parsing Text-To-SQL

Improving Text-to-SQL with Schema Dependency Learning

no code implementations7 Mar 2021 Binyuan Hui, Xiang Shi, Ruiying Geng, Binhua Li, Yongbin Li, Jian Sun, Xiaodan Zhu

In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.


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