View a PDF of the paper titled “BriLLM: Brain-inspired Large Language Model,” authored by Hai Zhao, Hongqiu Wu, Dongjie Yang, Anni Zou, and Jiale Hong.
Abstract:This paper reports the first brain-inspired large language model (BriLLM). This is a non-Transformer, non-GPT, non-traditional machine learning input-output controlled generative language model. The model is based on the Signal Fully-connected flowing (SiFu) definition on the directed graph in terms of the neural network, and has the interpretability of all nodes on the graph of the whole model, instead of the traditional machine learning model that only has limited interpretability at the input and output ends. In the language model scenario, the token is defined as a node in the graph. A randomly shaped or user-defined signal flow flows between nodes on the principle of “least resistance” along paths. The next token or node to be predicted or generated is the target of the signal flow. As a language model, BriLLM theoretically supports infinitely long n-gram models when the model size is independent of the input and predicted length of the model. The model’s working signal flow provides the possibility of recall activation and innate multi-modal support similar to the cognitive patterns of the human brain. At present, we released the first BriLLM version in Chinese, with 4000 tokens, 32-dimensional node width, 16-token long sequence prediction ability, and language model prediction performance comparable to GPT-1. More computing power will help us explore the infinite possibilities depicted above.
Submission History
From: Dongjie Yang
[v1] Fri, 14 Mar 2025 11:08:30 UTC (3,372 KB)
[v2] Mon, 7 Apr 2025 11:09:39 UTC (3,372 KB)
[v3] Wed, 21 May 2025 15:02:30 UTC (3,372 KB)
### Introduction to BriLLM: A Groundbreaking Language Model
The field of artificial intelligence (AI) is undergoing rapid evolution, particularly in the realm of natural language processing (NLP). Traditional models like GPT and Transformers have dominated this sector, yet researchers are continually exploring novel approaches. One such innovative model is BriLLM (Brain-inspired Large Language Model). This model marks a significant departure from conventional frameworks, introducing a unique, brain-inspired architecture that opens new avenues for understanding language generation.
### Defining BriLLM: A Non-Traditional Approach
BriLLM is not just another contender in the crowded NLP space; it distinguishes itself as a non-Transformer, non-GPT, and non-traditional generative language model. Unlike typical models, which primarily rely on pre-trained transformers, BriLLM utilizes a methodology based on the Signal Fully-connected flowing (SiFu) concept. This approach represents a directed graph, where each token in the language model is conceptualized as a node.
### Signal Flow: The Core Mechanism
More Read
The foundational principle of BriLLM is its signal flow. Designed to mimic cognitive processes of the human brain, this framework allows for a more organic generation of text. Think of the signal flow as a dynamically created pathway that guides the model through various nodes—essentially, each node reflects a token. When predicting or generating the next token, the model activates the flow along paths of “least resistance,” enabling efficient exploration of possible outcomes.
### Enhanced Interpretability
One of the critical concerns with machine learning models is their interpretability. Often, traditional models have limited insight into their operations, primarily at input and output phases. BriLLM contrasts this limitation by offering a comprehensive interpretability feature across all nodes within its structure. This development allows researchers and users to delve deeper into the model’s reasoning, fostering greater confidence in its predictions and outputs.
### Infinite n-Gram Support
An exciting aspect of BriLLM is its theoretical support for infinitely long n-gram models. Here, the model size remains independent of the input and predicted lengths. This quality not only disrupts conventional understanding but also makes it possible to analyze and process far more complex language constructs without the limitations imposed by previous frameworks.
### Cognitive Patterns and Multi-Modal Support
What sets BriLLM apart in its capabilities is its uncanny resemblance to human cognitive patterns. The model’s signal flow facilitates recall activation, aligning with how humans retrieve information. This multi-modal support concept, which caters to various forms of data, represents a leap forward in making AI more intuitive, versatile, and aligned with human-like learning and processing criteria.
### Current Performance Metrics
As of now, BriLLM has launched its first version focused on the Chinese language. Key features include the ability to process 4,000 tokens, a node width of 32 dimensions, and the capability to predict sequences of up to 16 tokens in length. Notably, early performance metrics indicate results comparable to GPT-1, showcasing its potential as a serious player in the NLP arena.
### Looking Ahead: The Future of BriLLM
While the initial release provides a glimpse into BriLLM’s capabilities, the authors emphasize the need for more computational power to explore its infinite possibilities further. The implications of this research extend beyond mere advancements in NLP; they evoke intriguing questions about the intersection of AI, human cognition, and the boundaries of machine learning as we strive for models that better emulate the intricacies of human understanding.
In summary, BriLLM represents a promising frontier in the realm of advanced language models, challenging established paradigms and paving the way for future research. The model’s design philosophy could inspire an entire generation of innovative AI solutions, making it a noteworthy development for both researchers and practitioners of machine learning.
—
This article not only highlights the innovation behind BriLLM but also underscores its significance in redefining language models. It serves as a resource for anyone eager to understand the evolving landscape of artificial intelligence and the exciting prospects that lie ahead.
Inspired by: Source
