35 citations · 73 across the 25 of their papers we have counts for
12 papers · 1 filter
SheetBrain: A Neuro-Symbolic Agent for Accurate Reasoning over Complex and Large Spreadsheets
Ziwei Wang, Jiayuan Su, Mengyu Zhou +7
Understanding and reasoning over complex spreadsheets remain fundamental challenges for large language models (LLMs), which often struggle with accurately capturing the complex str…
TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models
Deyin Yi, Yihao Liu, Lang Cao +4
Tabular data analysis is crucial in many scenarios, yet efficiently identifying the most relevant data analysis queries and results for a new table remains a significant challenge.…
TwT: Thinking without Tokens by Habitual Reasoning Distillation with Multi-Teachers' Guidance
Jingxian Xu, Mengyu Zhou, Weichang Liu +3
Large Language Models (LLMs) have made significant strides in problem-solving by incorporating reasoning processes. However, this enhanced reasoning capability results in an increa…
TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models
Xinyi He, Yihao Liu, Mengyu Zhou +5
Tabular data are crucial in many fields and their understanding by large language models (LLMs) under high parameter efficiency paradigm is important. However, directly applying pa…
Table-LLM-Specialist: Language Model Specialists for Tables using Iterative Generator-Validator Fine-tuning
Junjie Xing, Yeye He, Mengyu Zhou +4
Language models such as GPT and Llama have shown remarkable ability on diverse natural language tasks, yet their performance on complex table tasks (e.g., NL-to-Code and data clean…
PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning
Jiaru Zou, Mengyu Zhou, Tao Li +2
Recent advances in fine-tuning large language models (LLMs) have greatly enhanced their usage in domain-specific tasks. Despite the success, fine-tuning continues to rely on repeat…
Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
Xinyi He, Mengyu Zhou, Xinrun Xu +9
Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL…
TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning
Yuan Sui, Jiaru Zou, Mengyu Zhou +4
Table reasoning tasks have shown remarkable progress with the development of large language models (LLMs), which involve interpreting and drawing conclusions from tabular data base…
Table Meets LLM: Can Large Language Models Understand Structured Table Data? A Benchmark and Empirical Study
Yuan Sui, Mengyu Zhou, Mingjie Zhou +2
Large language models (LLMs) are becoming attractive as few-shot reasoners to solve Natural Language (NL)-related tasks. However, the understanding of their capability to process s…
Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems
Jialiang Xu, Mengyu Zhou, Xinyi He +2
Numerical Question Answering is the task of answering questions that require numerical capabilities. Previous works introduce general adversarial attacks to Numerical Question Answ…
FormLM: Recommending Creation Ideas for Online Forms by Modelling Semantic and Structural Information
Yijia Shao, Mengyu Zhou, Yifan Zhong +5
Online forms are widely used to collect data from human and have a multi-billion market. Many software products provide online services for creating semi-structured forms where que…
Table Pre-training: A Survey on Model Architectures, Pre-training Objectives, and Downstream Tasks
Haoyu Dong, Zhoujun Cheng, Xinyi He +6
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs, and various other document types, a flurry of table pre-training frameworks have been prop…