16 citations · 19 across the 25 of their papers we have counts for
13 papers · 1 filter
A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning
Zhi Zhou, Yuhao Tan, Zenan Li +4
Test-time scaling seeks to improve the reasoning performance of large language models (LLMs) by adding computational resources. A prevalent approach within the field is sampling-ba…
FormalML: A Benchmark for Evaluating Formal Subgoal Completion in Machine Learning Theory
Xiao-Wen Yang, Zihao Zhang, Jianuo Cao +7
Large language models (LLMs) have recently demonstrated remarkable progress in formal theorem proving. Yet their ability to serve as practical assistants for mathematicians, fillin…
Data Selection for LLM Alignment Using Fine-Grained Preferences
Jia Zhang, Yao Liu, Chen-Xi Zhang +4
Large language models (LLMs) alignment aims to ensure that the behavior of LLMs meets human preferences. While collecting data from multiple fine-grained, aspect-specific preferenc…
Quantitative Estimation of Target Task Performance from Unsupervised Pretext Task in Semi/Self-Supervised Learning
Lin-Han Jia, Si-Yu Han, Wen-Chao Hu +5
The effectiveness of unlabeled data in Semi/Self-Supervised Learning (SSL) depends on appropriate assumptions for specific scenarios, thereby enabling the selection of beneficial u…
Realistic Evaluation of TabPFN v2 in Open Environments
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2
Tabular data, owing to its ubiquitous presence in real-world domains, has garnered significant attention in machine learning research. While tree-based models have long dominated t…
TabularMath: Understanding Math Reasoning over Tables with Large Language Models
Shi-Yu Tian, Zhi Zhou, Wei Dong +5
Mathematical reasoning has long been a key benchmark for evaluating large language models. Although substantial progress has been made on math word problems, the need for reasoning…
NeSyGeo: A Neuro-Symbolic Framework for Multimodal Geometric Reasoning Data Generation
Weiming Wu, Jin Ye, Zi-kang Wang +3
Obtaining large-scale, high-quality reasoning data is crucial for improving the geometric reasoning capabilities of multi-modal large language models (MLLMs). However, existing dat…
Detecting Scarce and Sparse Anomalous: Solving Dual Imbalance in Multi-Instance Learning
Lin-Han Jia, Lan-Zhe Guo, Zhi Zhou +3
In real-world applications, it is highly challenging to detect anomalous samples with extremely sparse anomalies, as they are highly similar to and thus easily confused with normal…
A Smooth Transition Between Induction and Deduction: Fast Abductive Learning Based on Probabilistic Symbol Perception
Lin-Han Jia, Si-Yu Han, Lan-Zhe Guo +4
Abductive learning (ABL) that integrates strengths of machine learning and logical reasoning to improve the learning generalization, has been recently shown effective. However, its…
LawGPT: Knowledge-Guided Data Generation and Its Application to Legal LLM
Zhi Zhou, Kun-Yang Yu, Shi-Yu Tian +6
Large language models (LLMs), both proprietary and open-source, have demonstrated remarkable capabilities across various natural language processing tasks. However, they face signi…
Bridging Internal Probability and Self-Consistency for Effective and Efficient LLM Reasoning
Zhi Zhou, Tan Yuhao, Zenan Li +4
Recent advancements in large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, single-shot inference often yields unreliable results for complex…
TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments
Zi-Jian Cheng, Zi-Yi Jia, Zhi Zhou +2
Tabular data is widely utilized in various machine learning tasks. Current tabular learning research predominantly focuses on closed environments, while in real-world applications,…
Vision-Language Model Selection and Reuse for Downstream Adaptation
Hao-Zhe Tan, Zhi Zhou, Yu-Feng Li +1
Pre-trained Vision-Language Models (VLMs) are becoming increasingly popular across various visual tasks, and several open-sourced VLM variants have been released. However, selectin…