4 citations · 11 across the 10 of their papers we have counts for
13 papers
Auto-Fill: Learning to Predict Missing Values Accurately with Specialist Language Models
Yurong Liu, Yeye He, Haoyu Dong +4
Predicting missing cell values in tabular data is a fundamental problem in data cleaning. While state-of-the-art reasoning models show great promise in predicting missing values in…
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…
Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning
Hanbing Liu, Lang Cao, Yuanyi Ren +5
Large language models (LLMs) show strong reasoning abilities but often produce unnecessarily long explanations that reduce efficiency. Although reinforcement learning (RL) has been…
MMTU: A Massive Multi-Task Table Understanding and Reasoning Benchmark
Junjie Xing, Yeye He, Mengyu Zhou +6
Tables and table-based use cases play a crucial role in many important real-world applications, such as spreadsheets, databases, and computational notebooks, which traditionally re…
SuperRL: Reinforcement Learning with Supervision to Boost Language Model Reasoning
Yihao Liu, Shuocheng Li, Lang Cao +6
Large language models are increasingly used for complex reasoning tasks where high-quality offline data such as expert-annotated solutions and distilled reasoning traces are often…
Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learning
Lang Cao, Jingxian Xu, Hanbing Liu +5
Tables are a fundamental medium for organizing and analyzing data, making table reasoning a critical capability for intelligent systems. Although large language models (LLMs) exhib…
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.…
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…
SpreadsheetLLM: Encoding Spreadsheets for Large Language Models
Haoyu Dong, Jianbo Zhao, Yuzhang Tian +8
Spreadsheets are characterized by their extensive two-dimensional grids, flexible layouts, and varied formatting options, which pose significant challenges for large language model…
KET-QA: A Dataset for Knowledge Enhanced Table Question Answering
Mengkang Hu, Haoyu Dong, Ping Luo +2
Due to the concise and structured nature of tables, the knowledge contained therein may be incomplete or missing, posing a significant challenge for table question answering (Table…
Vision Language Models for Spreadsheet Understanding: Challenges and Opportunities
Shiyu Xia, Junyu Xiong, Haoyu Dong +6
This paper explores capabilities of Vision Language Models on spreadsheet comprehension. We propose three self-supervised challenges with corresponding evaluation metrics to compre…
Reflection of Thought: Inversely Eliciting Numerical Reasoning in Language Models via Solving Linear Systems
Fan Zhou, Haoyu Dong, Qian Liu +3
Numerical reasoning over natural language has been a long-standing goal for the research community. However, cutting-edge language models have proven difficult to reliably generali…