Evaluating Synthetic Data in Multi-Document Extraction Tasks

Large language models (LLMs) have become quite popular for their ability to analyze text. However, evaluating their performance on real-world tasks can be tricky. One interesting task we can look at is called Multi-Insight Multi-Document Extraction (MIMDE). This task focuses on gathering useful information from a bunch of documents and connecting that information back to where it came from. Think of it as a detective trying to piece together clues from different sources. It’s crucial for things like analyzing feedback from surveys to improving healthcare services.

#The Importance of MIMDE

MIMDE tasks are not just fancy terms to throw around; they can have real-life applications. For instance, businesses can analyze customer feedback to make products better. In medicine, understanding patient experiences helps in improving treatments. We can find insightful lessons from survey responses, such as asking people if they think the voting age should stay at 18, and getting valuable feedback to shape policies.

#What We Did

In this study, we set out to see how well synthetic data (data made by computers) performs compared to human-generated data in MIMDE tasks. We made a framework for evaluating these tasks and created two types of datasets: one made from human responses and another generated by LLMs. We put 20 advanced LLMs to the test on both datasets to see how they fared in extracting insights.

#Creating Datasets

We needed a good way to collect data for our study. We had over 1,000 people take a survey, where they answered five hypothetical questions. They shared their thoughts through multiple-choice answers and free text explanations. We wanted to make sure we got a diverse range of insights, so we conducted pilot surveys to refine our questions and gather responses.

For the synthetic dataset, we used several LLMs like GPT-4 and GPT-3.5. We fed these models the same survey questions and told them to create answers based on a mix of insights. To keep things interesting, we added some randomness to their responses by varying their personalities and adjusting the way they expressed their thoughts.

#Evaluating the Performance

To see how well the LLMs did, we developed a set of Evaluation Metrics. We looked at True Positives (how many real insights were correctly identified), False Positives (how many incorrect insights were claimed), and False Negatives (how many real insights were missed). We also compared how well the models performed across the human and synthetic data.

#Insights and Findings

After running our evaluations, we found that the LLMs performed pretty well. On average, there was a strong positive correlation between the models' performance on human data and synthetic data when extracting insights. However, when it came to mapping those insights back to source documents, the results were far less promising for synthetic data.

#Human vs. Synthetic: The Reality Check

We learned that even though synthetic data can be useful for testing, it does not replicate human responses perfectly. For example, synthetic responses might be longer and contain more insights than human responses, which could make it tougher for the models when it comes to the mapping process. This inconsistency led us to suspect that synthetic data might not be a reliable substitute for human data in all aspects of MIMDE tasks.

#Lessons Learned

Throughout our research, we discovered that having a good method for comparing insights is vital. Using state-of-the-art LLMs proved more effective than traditional approaches. However, we found that some automatic evaluation methods still left room for improvement. If you want the best results, manual comparisons are the way to go.

#Future Directions

There are many exciting possibilities for research ahead. We could improve the synthetic data generation process by refining our prompting techniques and verifying the insights generated. It would also be intriguing to see how LLMs perform across different domains, like analyzing medical records or other kinds of reports, rather than just survey responses.

#Conclusion

In summary, the world of LLMs holds a lot of potential, especially in tasks like MIMDE. While synthetic data can be a game-changer for testing and evaluation, it’s not a complete stand-in for human data. As we continue to explore, the hope is to make these models even better at understanding and extracting valuable insights from various types of documents. So, let's keep going and see where this journey leads us!

And remember, if anyone tells you that synthetic data is as good as the real thing, just smile and nod. After all, we all know that nothing beats the human touch, not even the fanciest computer model!