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        <title>Hallucination Detection in Structured Query Generation via LLM Self-Debating</title>
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    <abstract>Hallucination remains a key challenge in applying large language models (LLMs) to structured query generation, especially for semi-private or domain-specific languages underrepresented in public training data. In this work, we focus on hallucination detection in these low-resource structured language scenarios, using Splunk Search Processing Language (SPL) as a representative case study. We start from analyzing real-world SPL generation to define hallucination in this context and introduce a comprehensive taxonomy. To enhance detection performance, we propose the Self-Debating framework, which prompts an LLM to generate contrastive explanations from opposing perspectives before rendering a final consistency judgment. We also construct a synthetic benchmark, SynSPL, to support systematic evaluation of hallucination detection in SPL generation. Experimental results show that Self-Debating consistently outperforms LLM-as-a-Judge baselines with zero-shot and chain-of-thought (CoT) prompts in SPL hallucination detection across different LLMs, yielding 5–10% relative gains in hallucination F1 scores on both real and synthetic datasets, and up to 260% improvement for LLaMA-3.1–8B. Besides hallucination detection on SPL, Self-Debating also achieves excellent performance on the FaithBench benchmark for summarization hallucination, demonstrating the strong generalization ability of Self-Debating, with OpenAI o1-mini achieving state-of-the-art performance. All these results consistently demonstrate the strong robustness and wide generalizability of Self-Debating.</abstract>
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%0 Conference Proceedings
%T Hallucination Detection in Structured Query Generation via LLM Self-Debating
%A Li, Miaoran
%A Chen, Jiangning
%A Xu, Minghua
%A Wang, Xiaolong
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F li-etal-2025-hallucination
%X Hallucination remains a key challenge in applying large language models (LLMs) to structured query generation, especially for semi-private or domain-specific languages underrepresented in public training data. In this work, we focus on hallucination detection in these low-resource structured language scenarios, using Splunk Search Processing Language (SPL) as a representative case study. We start from analyzing real-world SPL generation to define hallucination in this context and introduce a comprehensive taxonomy. To enhance detection performance, we propose the Self-Debating framework, which prompts an LLM to generate contrastive explanations from opposing perspectives before rendering a final consistency judgment. We also construct a synthetic benchmark, SynSPL, to support systematic evaluation of hallucination detection in SPL generation. Experimental results show that Self-Debating consistently outperforms LLM-as-a-Judge baselines with zero-shot and chain-of-thought (CoT) prompts in SPL hallucination detection across different LLMs, yielding 5–10% relative gains in hallucination F1 scores on both real and synthetic datasets, and up to 260% improvement for LLaMA-3.1–8B. Besides hallucination detection on SPL, Self-Debating also achieves excellent performance on the FaithBench benchmark for summarization hallucination, demonstrating the strong generalization ability of Self-Debating, with OpenAI o1-mini achieving state-of-the-art performance. All these results consistently demonstrate the strong robustness and wide generalizability of Self-Debating.
%R 10.18653/v1/2025.findings-emnlp.873
%U https://aclanthology.org/2025.findings-emnlp.873/
%U https://doi.org/10.18653/v1/2025.findings-emnlp.873
%P 16102-16113

Markdown (Informal)

[Hallucination Detection in Structured Query Generation via LLM Self-Debating](https://aclanthology.org/2025.findings-emnlp.873/) (Li et al., Findings 2025)

• Hallucination Detection in Structured Query Generation via LLM Self-Debating (Li et al., Findings 2025)

ACL

• Miaoran Li, Jiangning Chen, Minghua Xu, and Xiaolong Wang. 2025. Hallucination Detection in Structured Query Generation via LLM Self-Debating. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 16102–16113, Suzhou, China. Association for Computational Linguistics.