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20172022
most citedSelective Annotation Makes Language Models Better Few-Shot Learners

64 citations · 150 across the 11 of their papers we have counts for

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17 papers · 1 filter

cs.CL20221 cited

Augmenting Multi-Turn Text-to-SQL Datasets with Self-Play

Qi Liu, Zihuiwen Ye, Tao Yu +2

The task of context-dependent text-to-SQL aims to convert multi-turn user utterances to formal SQL queries. This is a challenging task due to both the scarcity of training data fro…

cs.CL202264 cited

Selective Annotation Makes Language Models Better Few-Shot Learners

Hongjin Su, Jungo Kasai, Chen Henry Wu +8

Many recent approaches to natural language tasks are built on the remarkable abilities of large language models. Large language models can perform in-context learning, where they l…

cs.CL20211 cited

Prefix-to-SQL: Text-to-SQL Generation from Incomplete User Questions

Naihao Deng, Shuaichen Chang, Peng Shi +2

Existing text-to-SQL research only considers complete questions as the input, but lay-users might strive to formulate a complete question. To build a smarter natural language inter…

cs.CL2021

An Exploratory Study on Long Dialogue Summarization: What Works and What's Next

Yusen Zhang, Ansong Ni, Tao Yu +6

Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challeng…

cs.CL2021

SummerTime: Text Summarization Toolkit for Non-experts

Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma +5

Recent advances in summarization provide models that can generate summaries of higher quality. Such models now exist for a number of summarization tasks, including query-based summ…

cs.CL2021

Logic-Consistency Text Generation from Semantic Parses

Chang Shu, Yusen Zhang, Xiangyu Dong +3

Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries. This is challenging due to two reason…