RAG: A Solution or a Vision for AI’s Hallucination Problem?
Generative AI models have been a development in the field of artificial intelligence. They can generate human-like text, making them extremely useful in a variety of applications, from chatbots to content creation. But these models are not without their flaws. One of the most important issues they face is the problem of hallucinations.
Hallucinations in this context refer to the imprecisions or deceptions that these models generate. For example, a generative AI model might create non-existent meeting attendees or suggest that certain topics were discussed in a conference call when they were not. These inaccuracies can be negligible, but they can also be severe, leading to major mistakes or distortion.
This problem creates a major challenge for businesses that want to incorporate AI into their operations. It weakens the dependability of these models and can lead to distrust among users. Also, it raises moral concerns about the use of AI, as these imprecisions can potentially lead to harm.
The Role of Retrieval Augmented Generation (RAG) in Addressing this Issue:
In response to this problem, some AI vendors have suggested a technical approach called Retrieval Augmented Generation (RAG). This approach is developed by data scientist Patrick Lewis. It involves recovering documents that are potentially pertinent to a question and then asking the model to generate answers given this additional context.
The idea behind RAG is that by providing the model with more situations, it will be able to generate more accurate and consistent responses. But while RAG has shown aptitude, it is not a perfect solution. It is less effective for tasks that require compound reasoning. There is also a risk that the model will ignore the recovered documents. Also, applying RAG at scale requires important computational resources, which can be a barrier for many businesses.
Looking Into the Future:
The hallucination problem in generative AI models is a compound issue that will require a multilayered solution. While methods like RAG offer a promising start, more research and development will be needed to fully address this problem. As the field of AI continues to evolve, it will be fascinating to see how this issue is tackled in the future.
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