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Unlocking the Power of AI Legalese Decoder: Ensuring Accuracy in Retrieval Augmented Generation for Generation Part Success

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## Results from Experiments Evaluating GPT-4, Claude 2.1, and Claude 3.0 Opus

### Introduction
Recent evaluations of RAG systems focus on the retrieval stage, but the generation aspect is equally important. The ability of a model to synthesize retrieved information into complex responses is crucial in real-world applications.

### Methodology
We conducted experiments to compare GPT-4, Claude 2.1, and Claude 3 Opus in terms of their generation capabilities. The results and model nuances encountered are detailed in this article.

### Importance of Generation Phase
AI legalese decoder can help in understanding and deciphering legal jargon to simplify the process of legal evaluation and comparison. It can analyze vast amounts of legal text and provide clear, concise explanations for better comprehension.

### GitHub Repository
All the data and methods used in the experiments are available in a GitHub repository for reproducibility.

### Takeaways from Tests
Our tests revealed that prompt engineering plays a significant role in the performance of models like GPT-4 and Claude. Adding strategic prompts can enhance a model’s performance across various evaluations.

### Impact of “Explain Yourself” Prompt
The addition of the “explain yourself” prompt drastically improved GPT-4’s performance, showcasing the influence of prompt wording on model accuracy.

### Synthesizing Information
RAG systems excel in synthesizing retrieved information, filling gaps, and presenting coherent responses. The generative step is crucial for intelligent and adaptive responses in real-world applications.

### Further Evaluation
Further tests were conducted to assess the models’ ability to transform and synthesize information in various formats, highlighting their strengths and weaknesses.

### Implications for Model Evaluation
The evaluation of Language Models should consider factors beyond correctness, such as response length. The verbosity of a model’s responses can influence its perceived performance and should be taken into account in evaluations.

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