arXiv:2605.29523v1 Announce Type: new Abstract: Large Language Models (LLMs) have advanced financial automation through Retrieval-Augmented Generation (RAG), yet hallucinations remain a critical barrier to deployment in high-stakes environments. Existing benchmarks focus on single-turn, English-centric tasks, leaving the multi-turn dynamics and linguistic-regulatory nuances of the Korean financial domain unaddressed. We introduce K-FinHallu, the first benchmark for hallucination detection in multi-turn Korean financial RAG. We construct multi-turn dialogues from authentic Korean financial docu
The rapid deployment of LLMs in financial applications necessitates robust hallucination detection benchmarks, especially as these models move beyond English and single-turn interactions.
Hallucinations remain a critical barrier to LLM deployment in high-stakes financial environments, and tailored benchmarks are crucial for mitigating this risk in specific linguistic and regulatory contexts.
The introduction of K-FinHallu provides a specific tool for evaluating and improving the reliability of multi-turn RAG systems in Korean finance, addressing a previously unaddressed gap.
- · Korean financial institutions
- · Korean AI developers
- · LLM safety and reliability researchers
- · AI governance and regulatory bodies
- · LLM providers with poor multi-lingual hallucination detection
- · Companies relying on untest-ed, multi-turn RAG in finance
Improved reliability and trustworthiness of LLM-powered financial automation in Korea.
Accelerated adoption of RAG-based AI solutions in other non-English, regulated financial markets.
Increased global competition among LLM providers to develop robust, multi-lingual hallucination detection capabilities.
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Read at arXiv cs.LG