The issue arises because the LLaMA model is not strictly adhering to the expected JSON format in its output. Instead of generating a clean JSON response, it includes additional text before and after the JSON structure, making it difficult to parse programmatically. This could be due to the model’s inherent behavior of completing text freely rather than strictly following a structured output format. Additionally, despite the prompt instructing the model to evaluate responsesummary based on relevance and word count, it does not reliably follow these criteria, potentially because the prompt is ambiguous, lacks specificity, or does not enforce a structured evaluation. This inconsistency can lead to outputs that are not fully aligned with the intended evaluation criteria. To resolve this, strategies such as fine-tuning the prompt, using output formatting constraints, or post-processing the response should be considered.
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There also seems to be a way to clean up the data assuming that the LLM does not return a complete JSON.