2 citations · 8 across the 9 of their papers we have counts for
9 papers
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models
Xinyu Pang, Ruixin Hong, Zhanke Zhou +5
Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (…
Abstraction-of-Thought Makes Language Models Better Reasoners
Ruixin Hong, Hongming Zhang, Xiaoman Pan +2
Abstract reasoning, the ability to reason from the abstract essence of a problem, serves as a key to generalization in human reasoning. However, eliciting language models to perfor…
CLOMO: Counterfactual Logical Modification with Large Language Models
Yinya Huang, Ruixin Hong, Hongming Zhang +6
In this study, we delve into the realm of counterfactual reasoning capabilities of large language models (LLMs). Our primary objective is to cultivate the counterfactual thought pr…
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning
Ruixin Hong, Hongming Zhang, Xinyu Pang +2
Logical reasoning has been an ongoing pursuit in the field of AI. Despite significant advancements made by large language models (LLMs), they still struggle with complex logical re…
Faithful Question Answering with Monte-Carlo Planning
Ruixin Hong, Hongming Zhang, Hong Zhao +2
Although large language models demonstrate remarkable question-answering performances, revealing the intermediate reasoning steps that the models faithfully follow remains challeng…
Automatically Predict Material Properties with Microscopic Image Example Polymer Compatibility
Zhilong Liang, Zhenzhi Tan, Ruixin Hong +3
Many material properties are manifested in the morphological appearance and characterized with microscopic image, such as scanning electron microscopy (SEM). Polymer miscibility is…
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure
Yinya Huang, Hongming Zhang, Ruixin Hong +3
In this paper, we propose a comprehensive benchmark to investigate models' logical reasoning capabilities in complex real-life scenarios. Current explanation datasets often employ…
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation
Ruixin Hong, Hongming Zhang, Xintong Yu +1
Knowing the reasoning chains from knowledge to the predicted answers can help construct an explainable question answering (QA) system. Advances on QA explanation propose to explain…
A Machine Learning Method for Material Property Prediction: Example Polymer Compatibility
Zhilong Liang, Zhiwei Li, Shuo Zhou +3
Prediction of material property is a key problem because of its significance to material design and screening. We present a brand-new and general machine learning method for materi…