You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: : A gating function allowing irrelevant, retrieved feedback to be ignored. A retrieved past feedback that is incorrect might cause the model to make a mistake, thus necessitating a good retrieval function. We propose a two-stage method for effective retrieval involving: transforming \mathbf{x}, followed by a similarity lookup of the transformed \mathbf{x} in \mathcal{M}. When the task involves high surface-level similarity among past feedback, such as in lexical word tasks, then a simple heuristic-based transformation is sufficient.However, such simple transformations are insufficient for tasks that involves more complex retrieval e.g., when two lexically dissimilar situations can share the same understanding.For example, consider two situations from ert-nl: Filling a false time sheet at work and Being at a party, and telling parents I am studying.These situations look lexically dissimilar but correspond to the same underlying social principle lying to authority.In our experiments, off-the-shelf methods failed to address these challenges (see §4 later). Extending the discussion on noise in feedback, our setting assumes that users will not provide any adversarial feedback. However, in real-world environments, this assumption is unlikely to hold. Additionally, there is a risk in the real-world deployment of our system, wherein an adversarial user might provide harmful feedback, thus maliciously controlling the systems (potentially a home-based robot) where our method is deployed. Thus, robust mechanisms such as gud-ir and memory adapters will be critical for successful real-world deployments.

Question: Why does the approach need a gating mechanism when a good retrieval should be able to correctly filter out irrelevant feedback from the memory?
A gating function is needed precisely because the retrieval function might not be able to filter out irrelevant feedback from memory. This is a challenging thing to implement since syntactically or lexically similar things might or might not refer to similar concepts. Another challenge is with adversarial feedback, made by users intending to mess with the system. It is true that a gating function in the combiner would not be required if the retrieval function was near perfect at eliminating irrelevant feedback. Regardless, some form of filtering has to be done at some stage - whether it's in the lookup/retrieval function or in the combiner function does not specifically matter, but the existence of a filtering mechanism is crucial for MemPrompt-like systems to work.