## 4 Experiment and Evaluation
### Discussion  
Similarly, chain-of-verification methods can also reduce hallucinations in LLMs. However, such methods do not directly affect the retrieval module, but instead act on the generation module, focusing on solving logical errors in LLMs and reducing hallucinations through multiple rounds of verification. The core of these methods lies in verifications after generating the answer to ensure the correctness of the output and reduce the possibility of errors. Fine-tuning LLMs requires substantial computing support. The application of an LLM in a specific domain currently needs the support of a well-organized systematic knowledge base.  
The findings of this study revealed that the BM-RAGAM framework surpasses baseline methodologies with respect to its response accuracy and interpretability. The framework exhibits substantial enhancements in reducing hallucinatory tendencies and bolstering the factual correctness of the responses generated. This achievement is attributed to the BM-RAGAM's capacity to dynamically incorporate domain-specific knowledge, thereby preserving and enhancing the quality of responses. Its hybrid joint retrieval approach, involving combining keyword-based and semantic-enhanced strategies with weighted attention mechanisms, ensures the prioritization and retrieval of the most pertinent information.  
However, certain limitations were observed. In the comparative analysis of the BM-RAGAM against Qwen2-72B, it was noted that the BM-RAGAM outputs were less seg  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Metrics** & **ROUGE-1** & **ROUGE-L** & **BERTScore** & **Embedding Cosine Similarity** \\ \hline Qwen2-P & 0.667 & 0.583 & 0.320 & \\ Qwen2-R & 0.444 & 0.389 & 0.323 & 0.894 \\ Qwen2-F & 0.533 & 0.467 & 0.323 & \\ RAG-P & 0.889 & 0.889 & 0.329 & \\ RAG-R & 0.444 & 0.444 & 0.305 & 0.91 \\ RAG-F & 0.592 & 0.592 & 0.318 & \\ BM-RAGAM-P & 0.9 & 0.9 & 0.386 & \\ BM-RAGAM-R & 0.5 & 0.5 & 0.366 & 0.91 \\ BM-RAGAM-F & 0.643 & 0.643 & 0.377 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: The ablation experiment used ocean fronts as an example.  
mented. This method, while ensuring a high contextual relevance and focus, may result in negative feelings and impede text comprehension for general users who prefer structured and well-segmented responses. This limitation likely arises from the attention mechanism's emphasis on relevance, which can reduce the granularity of information segmentation. Future work could address this issue by incorporating hierarchical generation strategies or explicit segmentation controls to achieve a balance between contextual focus and user comprehension.  
Although this study focused on ocean fronts and eddies, the framework's design ensures its adaptability to other domains. Additionally, integrating real-time updates to the vector knowledge base could enhance the system's performance for time-sensitive queries.  
From an ethical perspective, since the BM-RAGAM emphasizes the importance of data quality, incorporating mechanisms to ensure data provenance and transparency is crucial for ensuring the responsible use of retrieval-augmented systems.  
In summary, the BM-RAGAM achieved a notable balance between the retrieval efficiency and the response quality, presenting a beneficial advancement for domain-specific LLM applications. However, addressing its limitations, particularly in output segmentation and ethical considerations, will be critical for its broader adoption and impact. Future work should also explore multimodal extensions and real-time adaptability to further enhance its versatility and usability.