most citedFrom Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning

9 citations · 10 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2024

Sailor: Open Language Models for South-East Asia

Longxu Dou, Qian Liu, Guangtao Zeng +4

We present Sailor, a family of open language models ranging from 0.5B to 7B parameters, tailored for South-East Asian (SEA) languages. These models are continually pre-trained from…

cs.CL2024

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes

Dingzirui Wang, Longxu Dou, Xuanliang Zhang +2

Numerical reasoning is an essential ability for NLP systems to handle numeric information. Recent research indicates that fine-tuning a small-scale model to learn generating reason…

cs.CL20241 cited

A Survey of Table Reasoning with Large Language Models

Xuanliang Zhang, Dingzirui Wang, Longxu Dou +2

Table reasoning, which aims to generate the corresponding answer to the question following the user requirement according to the provided table, and optionally a text description o…

cs.CL2023

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning

Dingzirui Wang, Longxu Dou, Wenbin Zhang +2

Numerical reasoning is vital for natural language processing models to understand and process numerical information in real-world scenarios. Most current methods first generate the…

cs.CL2023

Controllable Data Augmentation for Context-Dependent Text-to-SQL

Dingzirui Wang, Longxu Dou, Wanxiang Che

The limited scale of annotated data constraints existing context-dependent text-to-SQL models because of the complexity of labeling. The data augmentation method is a commonly used…

cs.CL20239 cited

From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning

Qian Liu, Fan Zhou, Zhengbao Jiang +2

Fine-tuning language models on tasks with instructions has demonstrated potential in facilitating zero-shot generalization to unseen tasks. In this paper, we introduce a straightfo…