Summary of Rnns Are Not Transformers (yet): the Key Bottleneck on In-context Retrieval, by Kaiyue Wen et al.
RNNs are not Transformers (Yet): The Key Bottleneck on In-context Retrieval
by Kaiyue Wen, Xingyu Dang, Kaifeng Lyu
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper delves into the performance disparity between Recurrent Neural Networks (RNNs) and Transformers in addressing algorithmic challenges. The study explores whether RNNs’ memory-efficient nature for handling lengthy sequences can bridge the gap with Transformers, particularly when fueled by Chain-of-Thought (CoT) prompting. Theoretical analysis reveals that CoT boosts RNNs but fails to entirely close the performance gap. A primary bottleneck arises from RNNs’ inability to accurately retrieve information from context, even with CoT. For tasks like associative recall and determining graph tree-ness, RNNs are insufficiently expressive, whereas Transformers excel. Conversely, by enhancing RNNs’ in-context retrieval capabilities through techniques like Retrieval-Augmented Generation (RAG) and adding a single Transformer layer, RNNs can solve polynomial-time solvable problems with CoT, thereby closing the representation gap with Transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Recurrent Neural Networks (RNNs) and Transformers do in solving math-like problems. The researchers want to know if RNNs’ special ability to handle long sequences of information can help them catch up to Transformers, especially when they use a technique called Chain-of-Thought (CoT). They found that CoT helps RNNs but doesn’t completely close the gap with Transformers. A main problem is that RNNs have trouble getting the right information from the context, even with CoT. The researchers showed that for certain tasks, like remembering associations and identifying graph patterns, RNNs are not good enough, while Transformers can do it easily. On the other hand, by improving how well RNNs get information from context, they found that RNNs can solve math problems as well as Transformers. |
Keywords
* Artificial intelligence * Prompting * Rag * Recall * Retrieval augmented generation * Transformer
