2 citations · 5 across the 14 of their papers we have counts for
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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…
More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning
Yike Zhao, Simin Guo, Ziqing Yang +3
The reasoning capabilities of Large Language Models (LLMs) play a critical role in many downstream tasks, yet depend strongly on the quality of training data. Despite various propo…
From Task to Tutorial: An Automated GUI Framework for Excel Tutorial Document and Video Creation
Yuhang Xie, Jian Mu, Xiaojun Ma +9
Excel is one of the most widely used productivity tools across domains, offering rich functionality but also overwhelming users with its complexity. This creates a persistent deman…
Jupiter: Enhancing LLM Data Analysis Capabilities via Notebook and Inference-Time Value-Guided Search
Shuocheng Li, Yihao Liu, Silin Du +7
Large language models (LLMs) have shown great promise in automating data science workflows, but existing models still struggle with multi-step reasoning and tool use, which limits…
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.…
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…