3 citations · 3 across the 6 of their papers we have counts for
7 papers
Offline Guidance, Online Reasoning: Reusing LLM Feedback for Small Language Models
Bohan Zhang, Linan Yue, Weibo Gao +3
Large language models (LLMs) offer strong reasoning capabilities but are often costly to access through commercial APIs, while small language models (SLMs) are easier to deploy loc…
SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows
Jian Zhu, Yuzheng Zhang, Zeyao Ma +11
Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations s…
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
Sijia Luo, Xiaokang Zhang, Yuxuan Hu +6
Reinforcement Learning (RL) has become essential for eliciting complex reasoning capabilities in Large Language Models (LLMs). However, the substantial memory overhead of storing K…
FairACE: Achieving Degree Fairness in Graph Neural Networks via Contrastive and Adversarial Group-Balanced Training
Jiaxin Liu, Xiaoqian Jiang, Xiang Li +2
Fairness has been a significant challenge in graph neural networks (GNNs) since degree biases often result in un-equal prediction performance among nodes with varying degrees. Exis…
CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis
Bohan Zhang, Xiaokang Zhang, Jing Zhang +3
Current inference scaling methods, such as Self-consistency and Best-of-N, have proven effective in improving the accuracy of LLMs on complex reasoning tasks. However, these method…
SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation
Zeyao Ma, Bohan Zhang, Jing Zhang +6
We introduce SpreadsheetBench, a challenging spreadsheet manipulation benchmark exclusively derived from real-world scenarios, designed to immerse current large language models (LL…
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios
Xiaokang Zhang, Sijia Luo, Bohan Zhang +12
We introduce TableLLM, a robust large language model (LLM) with 8 billion parameters, purpose-built for proficiently handling tabular data manipulation tasks, whether they are embe…