What are the differences between transformers and State Space Models algorithms in LArge Language models?
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Transformers and State Space Models (SSMs) represent two distinct approaches to handling sequences in large language models, each with its unique strengths and limitations. Transformers, known for their superior performance in various natural language processing tasks, rely on an attention mechanism that scales quadratically with sequence length. This computational cost limits their practicality for long sequences despite their ability to generate syntactically well-formed and semantically plausible text. The architecture of Transformers, while powerful, encounters limits in language modeling, particularly in data-efficient training and potentially in encoding the compositional rules of human language. On the other hand, SSMs are tailored for efficiently handling long sequences due to their nearly linear scaling in sequence length. They have shown impressive results in modeling long-range dependencies across various tasks. However, SSMs traditionally underperform compared to Transformers in language modeling tasks due to challenges in recalling earlier tokens and comparing tokens across sequences. Despite these challenges, recent advancements have narrowed the performance gap. For instance, the introduction of hybrid models that combine SSMs with attention mechanisms or specific layers designed to enhance their capabilities in language modeling has shown promising results. These hybrid models can outperform Transformers in certain benchmarks, offering improvements in computational efficiency and performance on long sequences. Moreover, innovations like SPADE and Gated State Space (GSS) layers augment SSMs' ability to capture global and local dependencies, respectively, demonstrating the potential for SSMs to complement or even surpass Transformer performance in specific scenarios. These developments indicate a trend towards leveraging the strengths of both architectures to address their respective weaknesses, aiming for models that are both computationally efficient and capable of handling the complexities of natural language.
Answers from top 9 papers
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| Papers (9) | Insight |
|---|---|
| State Space Models like GSS offer faster training, zero-shot generalization to longer inputs, and competitive performance compared to well-tuned Transformer-based models for large language modeling tasks. | |
28 Dec 2022 20 Citations | State Space Models struggle with recalling earlier tokens and comparing tokens across sequences in language modeling, but a hybrid H3-attention model outperforms Transformers in some tasks. |
31 Aug 2022 | Not addressed in the paper. |
15 Dec 2022 | Transformers excel in capturing local information efficiently, while State Space Models (SSMs) are tailored for computing global information effectively. SPADE combines both for long sequence modeling in language tasks. |
28 Dec 2022 | State Space Models struggle with recalling earlier tokens and comparing tokens across sequences, while Transformers excel in language modeling due to better hardware utilization and performance. |
| Transformers excel in Language Modeling tasks, while State Space Models (SSMs) offer long-range contextualization. The Block-State Transformer (BST) combines both for improved performance and efficiency in processing long sequences. | |
| Block-State Transformer (BST) combines State Space Models (SSMs) for long-range contextualization and Block Transformers for short-term sequence representation, outperforming traditional Transformer architectures in language modeling tasks. | |
2 Citations | Transformers excel in local information, while State Space Models (SSMs) are tailored for global information in long sequences, as combined in SPADE for efficient long sequence modeling. |
| Not addressed in the paper. |
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For NER tasks, transformers have shown superior performance over traditional models. Studies have demonstrated that domain-specific transformer models, such as PubMedBERT, outperform general transformer models in extracting meaningful information from clinical trial texts, a task that is crucial for advancing medical sciences. This is further supported by the comparison of transformer-based models (BERT, RoBERTa, XLNet) with non-transformer-based models (CRF, BiLSTM-CNN-CRF) across various domains, where transformer-based models consistently outperformed their counterparts, highlighting the impact of domain choice on performance irrespective of data size or model type.
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In comparison, State Space models, which are traditionally used in time series analysis and control systems, lack the sophisticated mechanisms that transformers possess for handling the sequential and contextual nature of language. While State Space models can model dependencies over time, they do not inherently account for the complex, contextual relationships and semantic nuances that are critical in sentiment analysis and NER tasks.
In summary, transformers offer a significant advantage over State Space models in both sentiment analysis and NER by effectively capturing long-range dependencies and contextual information, leading to improved accuracy and robustness across a variety of domains and languages.
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On the other hand, SSMs, originally designed for continuous signals, excel in modeling long-range dependencies with subquadratic runtime complexity, making them efficient for long sequences. Despite their efficiency, SSMs have struggled to match the performance of Transformers in language modeling tasks due to challenges in recalling earlier tokens and comparing tokens across sequences. However, innovations like the Block-State Transformer (BST) and SPADE have begun to bridge this gap. BST combines SSMs with block-wise attention for improved language modeling performance and speed, while SPADE integrates SSMs at the bottom layer to augment global information processing, enhancing the model's ability to handle long sequences without compromising on local information capture.
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