RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
(2402.18510)Abstract
This paper investigates the gap in representation powers of Recurrent Neural Networks (RNNs) and Transformers in the context of solving algorithmic problems. We focus on understanding whether RNNs, known for their memory efficiency in handling long sequences, can match the performance of Transformers, particularly when enhanced with Chain-of-Thought (CoT) prompting. Our theoretical analysis reveals that CoT improves RNNs but is insufficient to close the gap with Transformers. A key bottleneck lies in the inability of RNNs to perfectly retrieve information from the context, even with CoT: for several tasks that explicitly or implicitly require this capability, such as associative recall and determining if a graph is a tree, we prove that RNNs are not expressive enough to solve the tasks while Transformers can solve them with ease. Conversely, we prove that adopting techniques to enhance the in-context retrieval capability of RNNs, including Retrieval-Augmented Generation (RAG) and adding a single Transformer layer, can elevate RNNs to be capable of solving all polynomial-time solvable problems with CoT, hence closing the representation gap with Transformers.
Overview
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The paper compares RNNs (Recurrent Neural Networks) and Transformers, focusing on their ability to solve algorithmic problems and how Chain-of-Thought (CoT) prompting and in-context retrieval enhancements can affect their performance.
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It reveals that while CoT prompts improve RNNs, they still fall short of matching the representational capabilities of Transformers, particularly in in-context retrieval tasks.
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The study proposes two strategies to bridge this gap: introducing Retrieval-Augmented Generation (RAG) and a single Transformer layer into RNNs, which significantly boosts their in-context retrieval capacity.
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Experimental results show that these enhanced RNNs can achieve near-perfect accuracy in algorithmic tasks, notably in determining if a graph is a tree, reaching a performance level comparable to Transformers.
Closing the Representation Gap Between RNNs and Transformers in Algorithmic Problems
Introduction
Recurrent Neural Networks (RNNs) and Transformers represent two prevalent approaches in modeling sequential data. While RNNs are known for their memory efficiency, Transformers, powered by self-attention mechanisms, demonstrate superior performance across a wide array of tasks, especially those requiring complex information retrieval within the context. This paper focuses on dissecting the representation capabilities of RNNs vis-à-vis Transformers, specifically in the context of algorithmic problem-solving. It explore whether RNNs can match Transformers' prowess when provided with enhancements like Chain-of-Thought (CoT) prompting and techniques boosting their in-context retrieval capabilities.
CoT's Impact on RNNs and Transformers
Through a comprehensive theoretical analysis, the study reveals that while CoT indeed enhances RNNs' expressiveness, this improvement falls short of narrowing the representational divide between RNNs and Transformers. This inadequacy is rooted in RNNs' inherent limitations in performing in-context retrieval tasks—a capability Transformers excel in. The paper substantiates these claims by demonstrating RNNs' inability to solve specific algorithmic problems that necessitate in-context retrieval, such as associative recall and determining if a graph forms a tree.
Bridging the Gap: In-Context Retrieval Augmented Generation (RAG) and Architectural Enhancements
The pivotal contribution of this investigation lies in two proposed strategies to eliminate the representational chasm between RNNs and Transformers:
- In-Context RAG: Introducing Retrieval-Augmented Generation (RAG) and embedding a single Transformer layer within RNNs substantially ameliorates their in-context retrieval capacities. Remarkably, such enhancements enable RNNs to tackle all polynomial-time-solvable problems with CoT, effectively equating their representational power with that of Transformers.
- Hybrid RNN Architecture: Proposing a hybrid model that appends a single Transformer layer to an RNN, it was found that this minimalist modification significantly boosts the RNNs’ capability to engage in in-context retrieval, thus elevating their performance in algorithmic problem solving to match that of Transformers.
Experimental Validation
The paper also includes an experimental segment where models were trained on a task designed to assess their graph understanding capabilities, specifically determining if a given graph is a tree (IsTree). The findings corroborated the theoretical analysis, as RNNs enhanced with either In-Context RAG or a single Transformer layer exhibited near-perfect accuracy, mirroring the performance of standard Transformers.
Conclusion and Future Perspectives
This investigation delineates a roadmap to bolstering RNNs' representation power to align with that of Transformers, particularly in the realm of algorithmic problem solving. While augmenting RNNs with CoT alone does not suffice, integrating retrieval augmentation or incorporating a single Transformer layer presents a promising avenue towards bridging the representational divide. These insights not only deepen our understanding of the intrinsic capabilities and limitations of these models but also open new frontiers for future research exploring optimal architectural configurations and enhancements for sequential data modeling.
This scholarly effort underscores the intrinsic limitations of RNNs in the sphere of in-context retrieval and algorithmic reasoning, offering concrete methodologies to remediate these constraints and advance the field towards more versatile and powerful sequential models.
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