Papers by Ruixin Hong
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
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Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
Faithful Question Answering with Monte-Carlo Planning (2023.acl-long)
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| Challenge: | Existing approaches to answer questions using large language models lack the ability to faithfully follow the intermediate reasoning steps from the known premises to the answer. |
| Approach: | They propose a faithful question-answering task that uses a Monte-Carlo planning algorithm to produce faithful reasoning steps from the known premises to the answer. |
| Outcome: | The proposed task can produce valid and faithful reasoning steps compared with large language models with a much smaller model size. |
A Closer Look at the Self-Verification Abilities of Large Language Models in Logical Reasoning (2024.naacl-long)
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| Challenge: | Existing models of large language models struggle with complex logical reasoning problems. |
| Approach: | They propose to use large language models to identify their own errors to improve their models' performance. |
| Outcome: | The proposed models can identify logical fallacies accurately and improve by themselves. |
Assimilation and Accommodation: Task-Adaptive Hierarchical Abstraction for Solving Web Tasks (2025.findings-acl)
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| Challenge: | Existing methods focus on summarizing workflows, i.e., common sub-routines, which introduce excessive low-level details that distract models. |
| Approach: | They propose a framework that derives task-adaptive hierarchical abstraction from experience to enhance web task reasoning. |
| Outcome: | The proposed framework improves performance with competitive cost-efficiency on Mind2web and Webarena. |
METGEN: A Module-Based Entailment Tree Generation Framework for Answer Explanation (2022.findings-naacl)
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| Challenge: | Existing work on QA explanation proposes to explain the answers with entailment trees composed of multiple enlargement steps. |
| Approach: | They propose a Module-based Entailment Tree GENeration framework that has multiple modules and a reasoning controller. |
| Outcome: | The proposed framework outperforms state-of-the-art models on the standard benchmark with only 9% of the parameters. |
Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models (2025.coling-main)
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Xinyu Pang, Ruixin Hong, Zhanke Zhou, Fangrui Lv, Xinwei Yang, Zhilong Liang, Bo Han, Changshui Zhang
| Challenge: | Existing large language models (LLMs) fail due to lack of knowledge or incorrect knowledge application. |
| Approach: | They propose a knowledge-augmented framework that constructs a formula set to provide explicit physics knowledge and utilizes checklists to guide effective knowledge application. |
| Outcome: | The proposed framework achieves state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%. |
Abstraction-of-Thought Makes Language Models Better Reasoners (2024.findings-emnlp)
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| Challenge: | Abstract reasoning is a key to generalization in human reasoning, but eliciting language models to perform reasoning with abstraction remains unexplored. |
| Approach: | They propose a new structured reasoning format called Abstraction-of-Thought (AoT) this approach elicits language models to first contemplate on the abstract level before incorporating concrete details . |
| Outcome: | The proposed model outperforms the prevailing Chain-of-Thought (CoT) reasoning on 23 unseen tasks. |
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure (2022.emnlp-main)
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| Challenge: | Current explanation datasets often employ synthetic data with simple reasoning structures. |
| Approach: | They propose a comprehensive logical reasoning explanation form that incorporates three main components to better fit the human cognitive process. |
| Outcome: | The proposed model performs better than existing models on real-life scenarios, but is more challenging for the current models. |