Ruixin Hong

METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation

2022 · Findings of the Association for Computational Linguistics: NAACL 2022

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 the answers with entailment trees composed of multiple entailment …

Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models

2024 · arXiv (Cornell University)

Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To …