ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866918131811745792 |
|---|---|
| author | Zhu, Yuqicheng Potyka, Nico Hernández, Daniel He, Yuan Ding, Zifeng Xiong, Bo Zhou, Dongzhuoran Kharlamov, Evgeny Staab, Steffen |
| author_facet | Zhu, Yuqicheng Potyka, Nico Hernández, Daniel He, Yuan Ding, Zifeng Xiong, Bo Zhou, Dongzhuoran Kharlamov, Evgeny Staab, Steffen |
| contents | Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose ArgRAG, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). ArgRAG constructs a QBAF from retrieved documents and performs deterministic reasoning under gradual semantics. This allows faithfully explaining and contesting decisions. Evaluated on two fact verification benchmarks, PubHealth and RAGuard, ArgRAG achieves strong accuracy while significantly improving transparency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20131 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation Zhu, Yuqicheng Potyka, Nico Hernández, Daniel He, Yuan Ding, Zifeng Xiong, Bo Zhou, Dongzhuoran Kharlamov, Evgeny Staab, Steffen Artificial Intelligence Machine Learning Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sensitivity to noisy or contradictory evidence and opaque, stochastic decision-making. We propose ArgRAG, an explainable, and contestable alternative that replaces black-box reasoning with structured inference using a Quantitative Bipolar Argumentation Framework (QBAF). ArgRAG constructs a QBAF from retrieved documents and performs deterministic reasoning under gradual semantics. This allows faithfully explaining and contesting decisions. Evaluated on two fact verification benchmarks, PubHealth and RAGuard, ArgRAG achieves strong accuracy while significantly improving transparency. |
| title | ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation |
| topic | Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2508.20131 |