You: playing on the below, write a blog post exploring whether the two kinds of recall are all the kinds, linking this with the film Total Recall with Arnold Schwarznegger It is an established assumption that pattern-based models are good at precision, while learning based models are better at recall. But is that really the case? I argue that there are two kinds of recall: d-recall, reflecting diversity, and e-recall, reflecting exhaustiveness. I demonstrate through experiments that while neural methods are indeed significantly better at d-recall, it is sometimes the case that pattern-based methods are still substantially better at e-recall. Ideal methods should aim for both kinds, and this ideal should in turn be reflected in our evaluations. ChatGPT: Title: Total Recall Redefined: Dissecting D-Recall and E-Recall in the Age of AI Subtitle: A Deep Dive into the Diverse Worlds of Recall, Revisiting the Classic Film "Total Recall" with Arnold Schwarzenegger Introduction: It's an age-old debate in the world of artificial intelligence and natural language processing: which model is better for recall - pattern-based or learning-based? This discussion has always been framed around the idea that learning-based models excel at recall, while pattern-based models shine in the realm of precision. However, this assumption may be too simplistic, especially when we consider the possibility that there are multiple types of recall to explore. In this blog post, we will delve into two kinds of recall - d-recall (diversity) and e-recall (exhaustiveness) - and re-examine this debate through the lens of the classic sci-fi film "Total Recall" with Arnold Schwarzenegger. A New Perspective on Recall: To better understand the nuances of recall, let's break it down into two distinct types: 1. D-Recall (Diversity): This type of recall focuses on the range of different, relevant responses a model can generate. A high d-recall implies that a model is able to produce a wide variety of accurate and relevant answers. 2. E-Recall (Exhaustiveness): This type of recall is all about the completeness of the information retrieved. A model with high e-recall is able to provide a comprehensive and detailed response, covering all relevant aspects of a query. The distinction between these two types of recall is important, as it sheds light on the strengths and weaknesses of pattern-based and learning-based models. The "Total Recall" Connection: In the 1990 sci-fi classic "Total Recall," Arnold Schwarzenegger's character, Douglas Quaid, struggles with the concept of reality as his memories are manipulated and tampered with. This film serves as a fitting analogy for our exploration of recall types, as it forces us to question our assumptions and re-evaluate our understanding of the concept. D-Recall in Action: Neural methods, such as learning-based models, tend to excel at d-recall. They can generate a wide array of diverse and relevant responses, allowing for greater flexibility in handling complex and ambiguous queries. This can be seen as analogous to the shifting realities and diverse environments Quaid experiences in "Total Recall," as he navigates his way through the treacherous and uncertain world of manipulated memories. E-Recall Strikes Back: While neural methods are impressive in terms of d-recall, pattern-based models should not be dismissed when it comes to e-recall. In certain situations, pattern-based models can outperform their learning-based counterparts in terms of exhaustiveness, providing a more comprehensive and detailed response. Much like Quaid's quest for the truth in "Total Recall," e-recall searches for the most complete and accurate representation of reality. Balancing the Two Worlds of Recall: The ultimate goal in artificial intelligence and natural language processing is to create a model that excels at both d-recall and e-recall, providing diverse and exhaustive responses. This balance can be thought of as finding the true reality within the complex world of "Total Recall," a blend of both diverse experiences and comprehensive truth. Conclusion: As we re-evaluate the assumption that learning-based models are inherently superior for recall, it is important to remember that there are multiple types of recall, each with its own strengths and weaknesses. By recognizing and understanding the distinction between d-recall and e-recall, we can develop more effective models and evaluation methods, pushing the boundaries of AI and natural language processing even further. Just as Douglas Quaid ultimately uncovers the truth in "