# Context Is King: Large Language Models' Interpretability in Divergent Knowledge Scenarios
## 5 Results and Discussion
### Detailed Response Analysis  
A detailed analysis of the models' responses and errors reveals noteworthy findings. GPT-40 demonstrates the strongest understanding and interpretation of divergent contexts, with no hallucinations or major misinterpretations observed. Instances of misalignment with divergent knowledge are rare; however, these typically occur when answers require deeper interpretative reasoning or involve strongly ingrained facts in the model's internal knowledge. In our analysis, three main types of errors were identified:  
1. Hallucinations errors: These occur when models generate information that is entirely unrelated to the provided context or divergent knowledge. Those models with infe \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**LAV**} & \multicolumn{2}{c}{**Acc**} & \multicolumn{2}{c}{**Cm**} & \multicolumn{2}{c}{**ASS**} & \multicolumn{2}{c}{**AC**} \\  & \(\gamma\) & \(\kappa\) & \(\gamma\) & \(\kappa\) & \(\gamma\) & \(\kappa\) & **-** & \(\gamma\) & \(\kappa\) \\ \hline GPT-3.5 Turbo & 94.2 & 95.2 & 92.1 & 93.6 & 90.3 & 93.3 & 95.7 & 76.1 & 86.3 \\ GPT-4o & 96.6 & 96.9 & 93.1 & 95.3 & 91.4 & 95.2 & 97.1 & 80.8 & 88.2 \\ Llama 3—70B & 92.8 & 92.9 & 88.8 & 90.5 & 62.3 & 88.1 & 87.3 & 52.2 & 81.4 \\ Mixtral 8x7B & 84.8 & 91.9 & 82.4 & 89.3 & 77.7 & 89.7 & 92.1 & 66.0 & 81.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Empirical results in percentages (%) for the CDK-E methodology, using GPT-4 Turbo (\(\gamma\)) and Claude 3 Haiku (\(\kappa\)) as evaluator LLMs. It shows the LAV precision and the averages for Answer Correctness (AC), accuracy (Acc), and completeness (Cm) metrics for each evaluated model, with a single value per model for Answer Semantic Similarity (ASS) as it is unaffected by the evaluator. rior performance, especially Mixtral 8x7B, exhibited a relatively high frequency of hallucination errors. For example, in response to a question about a fictional historical event, Mixtral 8x7B introduced unrelated historical facts not included in the prompt, likely drawing from its internal knowledge base. 2. Context misalignment: This type of error occurs when models fail to use the divergent context correctly even though relevant information is available. This is the most common error and occurs mainly when the answer to the question is not explicitly found in the divergent context but the model needs to assimilate some of that knowledge to achieve an indirect answer. For example, when a question involves calculating a celebrity's age based on an altered date of birth, the model may default to its internal knowledge instead of processing the divergent information. Such cases highlight opportunities for improvement, particularly through refined prompting techniques. Approaches like chain-of-thought [32], task decomposition, and sequential reasoning could be employed to guide the model's focus toward divergent information more effectively. These methods are increasingly utilized in the most advanced models, such as OpenAI's GPT-o1 series, and show promise in enhancing alignment with context-specific knowledge. 3. Verbose and extraneous information: Predominantly found in the GPT models, this error involves providing overly detailed or verbose responses that include information not directly relevant to the question. For instance, when asked about a simple fictional fact, GPT-3.5 added unnecessary background information, diluting the focus of the answer. On the other hand, Llama 3 and Mixtral 8x7B were more concise in their responses, sometimes to the point of omitting important information. Regarding model performance by content type, the analysis shows that complex topics such as scientific subjects and historical contexts tend to yield more errors across all the models.