@inproceedings{teng-etal-2026-pathsoft,
title = "{P}ath{S}oft: Hybrid Soft Prompting with Ranked Reasoning Paths for Knowledge Graph Question Answering",
author = "Teng, Yan and
Zhou, Xiaoding and
Zhang, Jinshuo and
Lian, Ying and
Chen, Guoqiang and
Guan, Hongjiao and
Zhang, Weiyu and
Lu, Wenpeng",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.482/",
pages = "11063--11080",
ISBN = "979-8-89176-459-0",
abstract = "Large language models (LLMs) have demonstrated strong reasoning abilities, but hallucinations and unverifiable intermediate reasoning steps remain key challenges. In knowledge graph question answering (KGQA), entity-relation paths from knowledge graphs (KGs) provide structured and verifiable evidence for grounded reasoning. However, existing KG-enhanced LLM methods commonly linearize all retrieved reasoning paths into textual prompts, introducing noise and underexploiting their ordered structure. To address these limitations, we propose PathSoft, a hybrid soft prompting framework that combines textual path evidence with soft prompts. PathSoft first employs PathMLP to select reliable reasoning paths, and then uses PathSoftAdapter to convert the selected paths into continuous soft prompts while preserving their ordered path structure. Finally, the resulting soft prompt tokens are combined with the textual prompt at the embedding layer of the frozen LLM, enabling it to exploit both readable path evidence and path structure information. Experiments on WebQSP and CWQ show that PathSoft outperforms existing KGQA baselines. The code is available at https://github.com/tengyan1786-blip/PathSoft."
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<abstract>Large language models (LLMs) have demonstrated strong reasoning abilities, but hallucinations and unverifiable intermediate reasoning steps remain key challenges. In knowledge graph question answering (KGQA), entity-relation paths from knowledge graphs (KGs) provide structured and verifiable evidence for grounded reasoning. However, existing KG-enhanced LLM methods commonly linearize all retrieved reasoning paths into textual prompts, introducing noise and underexploiting their ordered structure. To address these limitations, we propose PathSoft, a hybrid soft prompting framework that combines textual path evidence with soft prompts. PathSoft first employs PathMLP to select reliable reasoning paths, and then uses PathSoftAdapter to convert the selected paths into continuous soft prompts while preserving their ordered path structure. Finally, the resulting soft prompt tokens are combined with the textual prompt at the embedding layer of the frozen LLM, enabling it to exploit both readable path evidence and path structure information. Experiments on WebQSP and CWQ show that PathSoft outperforms existing KGQA baselines. The code is available at https://github.com/tengyan1786-blip/PathSoft.</abstract>
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%0 Conference Proceedings
%T PathSoft: Hybrid Soft Prompting with Ranked Reasoning Paths for Knowledge Graph Question Answering
%A Teng, Yan
%A Zhou, Xiaoding
%A Zhang, Jinshuo
%A Lian, Ying
%A Chen, Guoqiang
%A Guan, Hongjiao
%A Zhang, Weiyu
%A Lu, Wenpeng
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F teng-etal-2026-pathsoft
%X Large language models (LLMs) have demonstrated strong reasoning abilities, but hallucinations and unverifiable intermediate reasoning steps remain key challenges. In knowledge graph question answering (KGQA), entity-relation paths from knowledge graphs (KGs) provide structured and verifiable evidence for grounded reasoning. However, existing KG-enhanced LLM methods commonly linearize all retrieved reasoning paths into textual prompts, introducing noise and underexploiting their ordered structure. To address these limitations, we propose PathSoft, a hybrid soft prompting framework that combines textual path evidence with soft prompts. PathSoft first employs PathMLP to select reliable reasoning paths, and then uses PathSoftAdapter to convert the selected paths into continuous soft prompts while preserving their ordered path structure. Finally, the resulting soft prompt tokens are combined with the textual prompt at the embedding layer of the frozen LLM, enabling it to exploit both readable path evidence and path structure information. Experiments on WebQSP and CWQ show that PathSoft outperforms existing KGQA baselines. The code is available at https://github.com/tengyan1786-blip/PathSoft.
%U https://aclanthology.org/2026.emnlp-main.482/
%P 11063-11080
Markdown (Informal)
[PathSoft: Hybrid Soft Prompting with Ranked Reasoning Paths for Knowledge Graph Question Answering](https://aclanthology.org/2026.emnlp-main.482/) (Teng et al., EMNLP 2026)
ACL
- Yan Teng, Xiaoding Zhou, Jinshuo Zhang, Ying Lian, Guoqiang Chen, Hongjiao Guan, Weiyu Zhang, and Wenpeng Lu. 2026. PathSoft: Hybrid Soft Prompting with Ranked Reasoning Paths for Knowledge Graph Question Answering. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 11063–11080, Budapest, Hungary. Association for Computational Linguistics.