2025 Large Models and Transformer Architecture: Technology Frontiers and Future Trends Report

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In the grand landscape of artificial intelligence, the Transformer architecture is undoubtedly a shining star. Its emergence has completely changed the development trajectory of many fields, including natural language processing and computer vision. The “2025 Large Models and Transformer Architecture: Technology Frontiers and Future Trends Report” delves into the origins, advantages, limitations, and future directions of the Transformer architecture, providing a comprehensive presentation of its core position and infinite potential in the AI field.

1. The Inspiration Behind the Birth of Transformer Architecture

The birth of the Transformer architecture is deeply inspired by the information processing mechanisms of the human brain. Over a long evolutionary process, the human brain has developed a highly efficient information processing system. As the number of neurons increases, the types become more diverse, the connections become more complex, and the brain regions continue to expand, the brain is able to efficiently process massive amounts of information under limited resource conditions. Among them, the attention mechanism plays a crucial role. It acts like a “spotlight” of the brain, precisely focusing limited computational resources on important tasks, allowing the brain to quickly analyze key information and make reasonable decisions.

In the field of artificial intelligence, researchers have drawn inspiration from the human brain’s attention mechanism to develop the “self-attention mechanism.” This mechanism calculates the similarity between different parts of the input sequence and assigns different weights to each part, thereby understanding the meaning of sentences more accurately. For example, when understanding a sentence, the self-attention mechanism can consider the content of the entire sentence and the relationships between each word, greatly enhancing the ability to comprehend information. It can be said that the self-attention mechanism in artificial intelligence and the attention mechanism in the human brain have a similar function in efficiently processing information and optimizing decision-making under limited resources. This clever borrowing of inspiration lays a solid theoretical foundation for the birth of the Transformer architecture.

2. The Rise of the Transformer Architecture

In 2017, the Google Brain team proposed the Transformer architecture in the groundbreaking paper “Attention Is All You Need.” Once released, it quickly rose to prominence in the field of natural language processing, dominating the landscape and gradually expanding into many other fields such as image processing and speech recognition.

The Transformer architecture mainly consists of two parts: the encoder and the decoder. The encoder includes components such as input embedding, positional encoding, multi-head attention, feedforward networks, residual connections, and layer normalization; the decoder includes output embedding, positional encoding, masked multi-head attention, encoder-decoder attention, feedforward networks, residual connections, and layer normalization, finally outputting the final result through a linear layer and a Softmax layer.

The core highlights of the Transformer architecture lie in the self-attention mechanism and the multi-head attention mechanism. The self-attention mechanism empowers the model with the ability to simultaneously compute the weights of the interrelationships between all positions in the input sequence and generate feature representations for each position based on these weights. Mathematically, the attention mechanism maps a query (Query) and a set of key-value pairs (Key-Value) to an output, where the output is the weighted sum of the values (Value), and the weights are calculated based on a compatibility function between the query and the corresponding key.

The multi-head attention mechanism is an innovative extension based on a single attention mechanism. By constructing a combination of multiple parallel attention mechanisms, it significantly broadens the model’s perspective. This allows the model to simultaneously focus on input information from multiple different angles, thereby capturing richer features and relationships. The multi-head attention mechanism not only enhances the model’s ability to learn dependencies within the sequence but also effectively alleviates the potential loss of effective resolution that may occur in a single attention mechanism, greatly improving the overall performance and accuracy of the model.

3. Application Scenarios of the Transformer Architecture

Language Models:

GPT Series: Such as GPT-3, GPT-4, etc., which possess astonishing language generation capabilities and versatility, capable of generating human-like text, answering various questions, and even participating in creative writing. For example, users can converse with GPT to obtain information, seek advice, etc.
BERT: Can be used for tasks such as text classification and question-answering systems. It is capable of understanding contextual semantics and accurately comprehending questions in question-answering tasks to provide high-quality answers.

Machine Translation: Google applies the Transformer in its search engine and translation services, enhancing the accuracy and quality of translations. Users can obtain more precise translation results when using Google Translate.

Text Prediction: The predictive text suggestions seen when entering information on a mobile keyboard may be the result of the Transformer. It can predict the next likely words based on the partially entered content.

Speech Recognition: Plays a role in the speech recognition of smart speakers, making voice assistants smarter and more practical. For example, it can more accurately recognize users’ voice commands and respond accordingly.

Cross-Domain Applications:

DALL·E: Generates images from text descriptions, showcasing the application of Transformers in the field of image generation.
GitHub Copilot: Assists developers by generating code snippets, improving programming efficiency.

Bioinformatics: Researchers use Transformers to analyze protein sequences, helping to predict protein structures and functions, which is of significant importance in drug development and disease research.

Music Generation: AI composition systems utilize the Transformer architecture to create stunning musical works.

Solving Mathematical Problems: Research by Meta AI found that Transformers can be used to solve problems related to finding global Lyapunov functions. For instance, by training models using backward generation techniques, new Lyapunov functions can be found for random dynamical systems with unknown stability, achieving accuracy rates exceeding 80%, while human mathematicians at the master’s level score less than 10% on this task.

Video Generation: OpenAI’s Sora model uses the Transformer architecture to create realistic and imaginative scenes based on text instructions, generating videos up to one minute long in various styles and formats. It can also generate videos from static images or expand existing videos by filling in missing frames.

Automated Prompt Engineering Systems: The PAS automated prompt engineering system proposed by Peking University and Baichuan Joint Laboratory is based on the Transformer architecture. It can effectively supplement user inputs concisely and performs far better than existing models in multiple benchmark tests, requiring less data. For example, when handling the question “If there are 10 birds on a tree and one is shot, how many birds are left on the ground?”, the PAS system guides the model to successfully avoid logical traps through supplementary prompts, demonstrating a clear reasoning process and providing the correct answer.

4. Significant Advantages of the Transformer Architecture

(1) Exceptional Ability to Handle Long-Distance Dependencies and Parallel Computation

The Transformer model utilizes positional encoding to assign order information to each element in the input sequence, allowing it to clearly distinguish elements at different positions and perform excellently in handling long-distance dependencies. Comparing the testing loss of the Transformer and LSTM under different parameter counts and context lengths reveals that the Transformer has a clear advantage in processing long contexts, better utilizing long context information. Moreover, as the parameters and context length increase, its performance improves significantly. Unlike RNN/LSTM, the Transformer can process all tokens simultaneously, effectively avoiding issues of information decay or disappearance, and can fully leverage the parallel computing capabilities of modern computational devices like GPUs, greatly enhancing training efficiency. For example, when processing sentences with hundreds of words, RNN needs to process word by word, while the Transformer can complete it all at once, significantly reducing processing time.

(2) Strong Driving Force for Efficient Training and Scaling of Models

The parallel computing advantage of the Transformer brings great convenience to model training, significantly improving training efficiency. When handling large-scale datasets, such as language model pre-training and machine translation tasks, it can complete training in a shorter time. For instance, the rapid pre-training of GPT series models benefits from this advantage of the Transformer architecture. The improvement in training efficiency further drives the continuous expansion of Transformer model scales, allowing larger models to learn richer features and complex patterns. In recent years, ultra-large-scale Transformer models such as GPT-3 and Megatron-LM have emerged, achieving groundbreaking results in natural language processing and continuously refreshing people’s understanding of language model capabilities.

(3) Wide Adaptability for Cross-Modal Applications

The Transformer architecture, with its high flexibility, has become the foundational framework for building advanced models in many non-natural language processing fields. It possesses a key capability: mapping data from different modalities to a unified feature representation space. In multi-modal tasks, such as text and image processing, the Transformer first converts text data into word vectors while transforming image data into pixel feature vectors. After this conversion process, feature vectors from different modalities can be efficiently processed and interacted within the same feature space. In contrast, earlier architectures like CNN, while excelling in processing visual data and having strong advantages in image processing tasks, have relatively weak capabilities for fusing cross-modal information; RNN/LSTM, although suitable for processing sequential data, especially text and speech data, have shortcomings in long-range dependency handling and efficiency in cross-modal tasks. The unified feature representation approach of the Transformer greatly reduces the complexity of fusing and comparing data from different modalities, effectively aiding multi-modal models in integrating and analyzing rich information from various data sources more efficiently.

5. Challenges Faced by the Transformer Architecture

Despite the tremendous success of the Transformer architecture, it is not without flaws and faces several challenges in its development process.

(1) Persistently High Computational Complexity

The computational complexity of the self-attention mechanism is quadratic, where N represents the sequence length and d denotes the dimension of token embeddings. This means that the computational complexity of the Transformer model increases quadratically with the length of the input sequence (number of tokens). When processing long sequence data, this high computational complexity can lead to significant resource consumption, placing high demands on hardware performance and somewhat limiting the model’s application range.

(2) High Training and Deployment Costs

As the scale of models based on the Transformer architecture continues to grow, the training and deployment costs have also increased significantly. In terms of computational resources, these models not only require substantial computing power to support complex operations but also have high demands for parallel processing capabilities. Training costs must cover high-performance GPUs and require extensive storage space. Moreover, as the sequence length increases, the quadratic scaling leads to a dramatic rise in memory usage, resulting in extremely high memory demands. This makes the costs of training and deploying Transformer models persistently high, significantly limiting their application in resource-constrained scenarios.

(3) Limitations in Long Sequence Applications

The direct impact of computational complexity and costs is that the Transformer is limited in long sequence applications. Although the Transformer can accurately capture short-distance textual relationships, its attention mechanism’s computational complexity increases quadratically with sequence length, making the computational costs of processing long texts difficult to bear. Therefore, most large models based on the Transformer architecture limit the supported context length to a certain range. Although researchers have recognized this limitation and made improvements to aspects like the attention mechanism to extend context lengths, there is still a certain gap compared to some emerging architectures.

6. Challengers to the Transformer Architecture

In the face of the limitations of the Transformer architecture, researchers are actively exploring innovations and proposing various potential alternative architectures, each with its unique characteristics, bringing new ideas and directions for the development of artificial intelligence.

(1) RetNet: A Model of Integrated Innovation

RetNet introduces a unique multi-scale retention mechanism to replace multi-head attention, skillfully integrating the advantages of RNN and Transformer. It features three computational paradigms: parallel, recurrent, and block recurrent representation. The parallel representation allows training to be parallelized, fully utilizing the powerful computation capabilities of GPU devices, accelerating training speed; the recurrent representation achieves efficient O(1) inference in terms of memory and computation, significantly reducing deployment costs and latency, and it does not require key-value caching techniques, simplifying the implementation process; the block recurrent representation can efficiently model long sequences by parallel encoding each local block to enhance computational speed while cyclically encoding the global block to save GPU memory.

The RetNet architecture has demonstrated significant advantages during training, saving 25-50% of memory compared to standard Transformers and achieving a 7-fold acceleration, also showing advantages in highly optimized Flash Attention. During inference, its inference latency is insensitive to batch size, achieving tremendous throughput. For a 7B model and 8k sequence length, its decoding speed is 8.4 times that of Transformers with key-value caching, while saving 70% of memory. However, as an architecture that incorporates RNN characteristics, the long-distance dependency modeling capability of RetNet still needs further validation, and there are relatively few practical applications at present, requiring more exploration and optimization.

(2) Mamba: A Bold Attempt at Multi-Framework Integration

Mamba innovatively combines the cyclic framework of recurrent neural networks (RNN), the parallel computation and attention mechanisms of Transformers, and the linear characteristics of state space models (SSM). It introduces a simple yet effective selection mechanism that can reparameterize the SSM based on the input, filtering out irrelevant information while indefinitely retaining necessary and relevant data. Mamba also includes a hardware-aware algorithm that uses scanning instead of convolution to compute the model cyclically, greatly enhancing computational speed. Subsequent iterative versions of Mamba-2 have built a robust theoretical framework using structured space-state duality (SSD/Structured Space-State Duality), enabling algorithms and system optimization techniques originally developed for Transformers to be applied to SSM.

The Mamba architecture exhibits outstanding performance in processing long sequence data with its linear growth in computational overhead and hardware-aware algorithm, significantly improving computational speed and performance. Compared to Transformers, the computational overhead of Mamba grows linearly with sequence length, allowing it to handle longer text sequences while drastically reducing computational costs. On an A100 GPU, Mamba can achieve a threefold increase in computational speed using scanning for cyclic computation, further enhancing its efficiency and performance in handling long sequence data. However, the Mamba architecture also faces issues such as memory loss, difficulty generalizing to different tasks, and underperformance in complex patterns compared to Transformer-based language models. Nevertheless, the open-source research community has proposed many improvement solutions for the Mamba architecture, and its performance is expected to be further optimized with ongoing research.

(3) RWKV: A New Breakthrough in RNN Variants

RWKV is an innovative variant of recurrent neural networks (RNN). Its architecture consists of a series of stacked residual blocks, each containing time-mixing and channel-mixing sub-blocks with a recurrent structure. Among them, the token shift operation is a significant feature of RWKV, allowing the model to flexibly control how much new information and old information are allocated to each head’s receiving, key, value, and gate vectors by linearly interpolating the current input and the previous time step’s input.

The RWKV architecture is in a state of continuous iteration. RWKV-5 introduces multi-head, matrix-valued states; RWKV-V6 incorporates a dynamic recursive mechanism based on low-rank adaptation (LoRA) to further optimize the Token Shift and time-mixing processes; the latest version, RWKV-7, adopts dynamic state evolution. As the versions continue to update, models based on the RWKV architecture are performing increasingly well on long sequence tasks, with constant memory usage, constant inference generation speed, and “infinite” context length, while providing free sentence embeddings and completely lacking self-attention mechanisms. In terms of resource usage, RWKV has lower demands for VRAM, CPU, GPU, etc., during operation and training, reducing computational demands by 10 to 100 times compared to larger context Transformers. Additionally, RWKV supports linear scalability to any context length, while Transformers scale quadratically. In terms of answer quality and generalization ability, RWKV performs comparably to the Transformer architecture. However, the RWKV base model is highly sensitive to the format of prompts, and variations in prompt wording can lead to significant differences in output results, which affects the stability and generality of the model’s use. Moreover, due to the design of the architecture, RWKV models perform poorly on tasks requiring recall and need to sort prompts reasonably to ensure better understanding and execution of tasks.

(4) Hyena: A New Attempt at Efficient Low Complexity

Hyena is defined by two efficient quadratic primitive recursive operators—interwoven implicit parameterized long convolution and data-controlled gating—constructing an efficient, flexible, and low-complexity alternative algorithm to the attention function in the Transformer architecture. The Hyena operator defines two efficient sub-quadratic basic operations: implicit long convolution and data-controlled diagonal matrix multiplication. The recursion depth determines the operator size, and Hyena can be expressed as a product of data-related diagonal and Toeplitz matrices, exhibiting sub-linear parameter scaling, unrestricted context, and lower time complexity than the attention mechanism, with a time complexity of O(n log(n)) instead of O(n²).

In practical applications, Hyena can significantly narrow the gap with the attention mechanism, achieving the same effect with a smaller computational budget. When the sequence length is 2K, Hyena reduces the training computational load by 20%, reaching Transformer quality; when the sequence length is 8K, the speed of the Hyena operator is twice that of highly optimized attention; and when the sequence length is 64K, the speed reaches 100 times. However, Hyena operations do not support masks (used in the pre-training modeling process of large language models), making it less flexible for generative pre-training modeling. Currently, follow-up applications of Hyena are relatively few, and future application spaces need further exploration and validation.

(5) Linear Attention Mechanism: An Important Direction for Improving Transformers

The linear attention mechanism reduces the time complexity of the traditional attention mechanism’s Softmax operation to linear (O(N)), effectively improving the parallel performance of Transformer models and reducing complexity, offering certain advantages in computational efficiency and model expressiveness. Currently, models such as Agent Attention, TransNormerLLM, and MiniMax-01 have made some progress in this area.

Agent Attention introduces a set of additional proxy vectors A into the traditional attention module, achieving efficient aggregation of information from keys K and values V and effectively broadcasting this information back to the query vector Q. This design not only significantly enhances computational efficiency but also retains the powerful capability of modeling global context. It successfully integrates traditional Softmax attention with linear attention, forming a new attention paradigm that performs excellently across various visual Transformer models and different visual tasks, particularly demonstrating remarkable effectiveness in handling high-resolution scenarios. Additionally, Agent Attention can be applied to pre-trained large-scale diffusion models, effectively accelerating the image generation process and significantly improving the quality of generated images.

TransNormerLLM, developed by the Shanghai Artificial Intelligence Laboratory and OpenNLPLab, is the first linear attention Transformer large model that completely discards the traditional Softmax attention mechanism in favor of a linear attention mechanism, decomposing Softmax attention into multiple linear operations, thus reducing computational complexity from quadratic to linear, greatly enhancing the model’s efficiency and enabling it to handle longer sequences. To further improve the computational efficiency of linear attention, TransNormerLLM introduces Lightning Attention technology, which segments the input data into multiple blocks for separate computation, reducing memory access times and increasing computational speed. The research team claims that Lightning Attention can double the speed of linear attention during training and reduce memory usage by four times through IO perception.

The MiniMax-01 series models first extend the linear attention mechanism to the level of commercial models. The MiniMax-Text-01 architecture structurally integrates linear attention and Softmax attention mechanisms. By using linear attention, the computational complexity of the native Transformer can be significantly reduced from O(N²) to O(N). Based on Lightning Attention, MiniMax also proposes a hybrid-lightning method, replacing Lightning Attention with Softmax attention every 8 layers, which addresses the efficiency issues of Softmax attention while enhancing the scaling capability of Lightning Attention.

However, linear attention still has certain gaps compared to Softmax attention in modeling long-distance dependencies, and relevant research is currently focused on addressing this issue to further enhance the performance of linear attention mechanisms.

(6) DeepSeek: An Innovative Pioneer in Large Language Models

DeepSeek, as an important player in the large language model field, showcases unique ideas and potential in architecture design, technological innovation, and practical applications, committed to enhancing performance while breaking through the limitations of traditional models.

The core of DeepSeek lies in its innovative architecture design based on mixture of experts (MoE). For example, DeepSeek-V3 is a model with a parameter count reaching 671 billion, with an active scale of 37 billion. It achieves the efficiency of large-scale MoE training through meticulously designed load balancing strategies and training objectives. During training, leveraging the collaborative design of algorithms, frameworks, and hardware ensures that the model can fully utilize computing resources, enhancing training efficiency. Additionally, DeepSeek introduces innovative methods to extract reasoning capabilities from the DeepSeek-R1 series models, enhancing the model’s reasoning performance while maintaining effective control over output style and length. Moreover, the model employs advanced technologies such as multi-head latent attention (MLA) to reduce memory usage, further optimizing the model’s operational efficiency.

In terms of performance advantages, DeepSeek excels. In numerous evaluation tasks, it has reached a leading level among open-source models (SOTA), even competing with top proprietary models. In knowledge-based tasks, such as MMLU (Massive Multi-task Language Understanding Evaluation) and GPQA (General Purpose Question Answering), DeepSeek demonstrates strong knowledge reserves and understanding capabilities; in mathematics competition tasks, such as AIME 2024 (American Mathematics Invitational Examination) and CNMO 2024 (China Mathematics Olympiad simulation evaluation), it also performs excellently, reflecting its good logical reasoning and problem-solving abilities; in code generation tasks, DeepSeek can generate high-quality, compliant code to meet various developer needs. Furthermore, compared to other models of similar levels, DeepSeek’s training costs are significantly lower; for instance, the training cost of DeepSeek-V3 is only 9% of Claude-3.5-Sonnet. Its generation speed has also increased from 20 TPS to 60 TPS, providing users with a smoother interactive experience. Additionally, DeepSeek offers competitively priced API services, lowering the threshold for developers and enterprises to use, and all series of models are open-source and free for commercial use, greatly promoting the dissemination of technology and collaborative innovation in the community.

However, DeepSeek is not perfect. In practical applications, there are areas needing improvement. For instance, the model shows biases in self-awareness; DeepSeek-V3 has incorrectly referred to itself as ChatGPT, indicating that its identity recognition and information accuracy need optimization. Regarding prompt adaptability, DeepSeek is sensitive to prompt formats, and different prompt phrasing can lead to significant variations in output results, which somewhat affects the stability and generality of the model’s use. In terms of functional expansion, DeepSeek’s performance in complex tasks such as multi-modal information processing, voice communication, and video understanding still has considerable room for improvement, as its capabilities in these areas are relatively weak and may not meet diverse user needs. Furthermore, when dealing with certain complex or specific issues, DeepSeek may provide incorrect answers, affecting its application effectiveness in specialized fields and high-precision tasks.

Overall, DeepSeek has made significant progress in the field of large language models through innovative architecture and technology, providing new ideas and directions for industry development. Despite some existing shortcomings, with continuous technological iteration and optimization, it is expected to further enhance performance, expand application scenarios, and play a greater role in the field of artificial intelligence.

7. Future Prospects of the Transformer Architecture

Currently, the future development of the Transformer architecture mainly has two paths. One path is to be replaced by more advanced new architectures; emerging architectures such as RetNet and Mamba exhibit potential advantages in computational complexity, memory usage, and inference speed, which may become mainstream architectures in the future, driving new leaps in artificial intelligence technology. The other path involves upgrading based on the existing architecture, such as optimizing attention mechanisms, which can effectively reduce computational complexity and improve model efficiency, allowing it to perform better under existing resource conditions.

From the overall development direction of AI large models, on one hand, researchers may explore entirely new foundational theories and model architectures, fundamentally overturning the current technological system and bringing unprecedented innovative breakthroughs. On the other hand, there will be in-depth exploration of potential within the existing technological framework, such as optimizing parameter efficiency to achieve better performance with fewer parameters; developing smarter training methods to improve training efficiency and model quality; and reducing dependence on data and computational power, making AI technology more sustainable. Regardless of the chosen path, the ultimate goal is to achieve higher performance, stronger generalization capabilities, and lower resource consumption, promoting the widespread application of AI technology in more practical scenarios, bringing AI closer to people’s lives, and achieving sustainable and inclusive development.

Academician Zhang Yaqin believes that the Transformer may be gradually restructured by new technologies within the next five years; Andrej Karpathy boldly predicts that Transformers are expected to surpass the human brain, etc. These views and studies indicate that in the future, with continuous technological advancement, the Transformer architecture and its alternative architectures will continue to evolve and improve. Their mutual competition and integration will inject continuous momentum into the development of artificial intelligence, creating more exciting possibilities. Whether in natural language processing to achieve more precise and intelligent interactions, or in computer vision to bring more powerful image understanding and generation capabilities, the Transformer architecture and its related technologies will play a crucial role in leading artificial intelligence towards a more brilliant future.

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