Mitigating LLM Hallucinations Using a Multi-Agent Framework
The rapid advancement of Large Language Models (LLMs) has led to substantial investment in enhancing their capabilities and expanding their feature sets. Despite these developments, a critical gap remains between model sophistication and their dependable deployment in real-world applications. A key...
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MDPI AG
2025-06-01
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| Online Access: | https://www.mdpi.com/2078-2489/16/7/517 |
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| author | Ahmed M. Darwish Essam A. Rashed Ghada Khoriba |
| author_facet | Ahmed M. Darwish Essam A. Rashed Ghada Khoriba |
| author_sort | Ahmed M. Darwish |
| collection | DOAJ |
| description | The rapid advancement of Large Language Models (LLMs) has led to substantial investment in enhancing their capabilities and expanding their feature sets. Despite these developments, a critical gap remains between model sophistication and their dependable deployment in real-world applications. A key concern is the inconsistency of LLM-generated outputs in production environments, which hinders scalability and reliability. In response to these challenges, we propose a novel framework that integrates custom-defined, rule-based logic to constrain and guide LLM behavior effectively. This framework enforces deterministic response boundaries while considering the model’s reasoning capabilities. Furthermore, we introduce a quantitative performance scoring mechanism that achieves an 85.5% improvement in response consistency, facilitating more predictable and accountable model outputs. The proposed system is industry-agnostic and can be generalized to any domain with a well-defined validation schema. This work contributes to the growing research on aligning LLMs with structured, operational constraints to ensure safe, robust, and scalable deployment. |
| format | Article |
| id | doaj-art-1f3d886202ee43bca7515dedcb9c3ac8 |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-1f3d886202ee43bca7515dedcb9c3ac82025-08-20T03:58:29ZengMDPI AGInformation2078-24892025-06-0116751710.3390/info16070517Mitigating LLM Hallucinations Using a Multi-Agent FrameworkAhmed M. Darwish0Essam A. Rashed1Ghada Khoriba2School of Information Technology and Computer Science, Nile University, Giza 3242020, EgyptGraduate School of Information Science, University of Hyogo, Kobe 650-0047, JapanSchool of Information Technology and Computer Science, Nile University, Giza 3242020, EgyptThe rapid advancement of Large Language Models (LLMs) has led to substantial investment in enhancing their capabilities and expanding their feature sets. Despite these developments, a critical gap remains between model sophistication and their dependable deployment in real-world applications. A key concern is the inconsistency of LLM-generated outputs in production environments, which hinders scalability and reliability. In response to these challenges, we propose a novel framework that integrates custom-defined, rule-based logic to constrain and guide LLM behavior effectively. This framework enforces deterministic response boundaries while considering the model’s reasoning capabilities. Furthermore, we introduce a quantitative performance scoring mechanism that achieves an 85.5% improvement in response consistency, facilitating more predictable and accountable model outputs. The proposed system is industry-agnostic and can be generalized to any domain with a well-defined validation schema. This work contributes to the growing research on aligning LLMs with structured, operational constraints to ensure safe, robust, and scalable deployment.https://www.mdpi.com/2078-2489/16/7/517spoken dialogue systemsevaluation and metricstask-orientedbias/toxicityfactualityapplications |
| spellingShingle | Ahmed M. Darwish Essam A. Rashed Ghada Khoriba Mitigating LLM Hallucinations Using a Multi-Agent Framework Information spoken dialogue systems evaluation and metrics task-oriented bias/toxicity factuality applications |
| title | Mitigating LLM Hallucinations Using a Multi-Agent Framework |
| title_full | Mitigating LLM Hallucinations Using a Multi-Agent Framework |
| title_fullStr | Mitigating LLM Hallucinations Using a Multi-Agent Framework |
| title_full_unstemmed | Mitigating LLM Hallucinations Using a Multi-Agent Framework |
| title_short | Mitigating LLM Hallucinations Using a Multi-Agent Framework |
| title_sort | mitigating llm hallucinations using a multi agent framework |
| topic | spoken dialogue systems evaluation and metrics task-oriented bias/toxicity factuality applications |
| url | https://www.mdpi.com/2078-2489/16/7/517 |
| work_keys_str_mv | AT ahmedmdarwish mitigatingllmhallucinationsusingamultiagentframework AT essamarashed mitigatingllmhallucinationsusingamultiagentframework AT ghadakhoriba mitigatingllmhallucinationsusingamultiagentframework |