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Hongjian Gu

Alumni

Publications

Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang
Mohammad Alomrani
Hongjian Gu
Jiaming Zhou
Yaochen Hu
B. Wang
Qun Liu
Mark Coates
Yingxue Zhang
Jianye Hao
2025-07-04
KDD.org/2025/Workshop/SKnow-LLM (présentation orale)
openreview.net
openreview.net
Robust and Interpretable Relational Reasoning with Large Language Models and Symbolic Solvers
Ge Zhang
Mohammad Alomrani
Hongjian Gu
Jiaming Zhou
Yaochen Hu
B. Wang
Qun Liu
Mark Coates
Yingxue Zhang
Jianye Hao
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational rea… (voir plus)soning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning by decomposing the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. Subsequently, PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers. Experimental evaluations on four benchmark datasets, demanding long reasoning chains, demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (maximum 21.3\%) without necessitating fine-tuning or extensive LLM calls. Furthermore, as opposed to prior neuro-symbolic methods, PoT exhibits improved resilience against LLM errors by leveraging the compositional nature of graphs.
2025-06-30
ICML.cc/2025/Workshop/R2-FM (poster)
openreview.net
openreview.net
Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang
Mohammad Alomrani
Hongjian Gu
Jiaming Zhou
Yaochen Hu
Bin Wang
Qun Liu
Mark Coates
Yingxue Zhang
Jianye Hao
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational rea… (voir plus)soning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning by decomposing the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. Subsequently, PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers. Experimental evaluations on four benchmark datasets, demanding long reasoning chains, demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (maximum 21.3%) without necessitating fine-tuning or extensive LLM calls. Furthermore, as opposed to prior neuro-symbolic methods, PoT exhibits improved resilience against LLM errors by leveraging the compositional nature of graphs.
2024-12-23
ArXiv (prépublication)
arxiv.org
arxiv.org
Path-of-Thoughts: Extracting and Following Paths for Robust Relational Reasoning with Large Language Models
Ge Zhang
Mohammad Alomrani
Hongjian Gu
Jiaming Zhou
Yaochen Hu
B. Wang
Qun Liu
Mark Coates
Yingxue Zhang
Jianye Hao
Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational rea… (voir plus)soning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework designed to tackle relation reasoning by decomposing the task into three key stages: graph extraction, path identification, and reasoning. Unlike previous approaches, PoT efficiently extracts a task-agnostic graph that identifies crucial entities, relations, and attributes within the problem context. Subsequently, PoT identifies relevant reasoning chains within the graph corresponding to the posed question, facilitating inference of potential answers. Experimental evaluations on four benchmark datasets, demanding long reasoning chains, demonstrate that PoT surpasses state-of-the-art baselines by a significant margin (maximum 21.3%) without necessitating fine-tuning or extensive LLM calls. Furthermore, as opposed to prior neuro-symbolic methods, PoT exhibits improved resilience against LLM errors by leveraging the compositional nature of graphs.
2024-12-23
ArXiv (prépublication)
doi.org
arxiv.org