Big language models are shrouded in mystery. How much do you know about the five major misunderstandings?

With the rapid development of artificial intelligence technology, large language models have shown great application potential in various fields. However, there are some common misunderstandings about large language models, which may lead to misunderstandings of model performance and improper application. This article will explore five misunderstandings about large language models in depth to help model users more accurately understand the characteristics and limitations of large language models.

This article explores five common misunderstandings about large language models. First, the bigger the model, the better. Increasing parameters may lead to over-parameterization and overfitting, and large models require huge computing resources. Second, computing power input is not completely positively correlated with model performance. When computing power input reaches a certain level, its marginal effect will gradually weaken. Third, although large models can process large amounts of text data, they do not have real understanding and reasoning capabilities, and their output is based on statistical and probabilistic methods. Fourth, large models are not universal tools. Specific areas require targeted optimization and are difficult to explain in some scenarios. Finally, large models need to be continuously updated to adapt to changes in data, technological advances, laws and regulations, and changes in user needs.

1. Myth 1: The bigger the model, the better; the more parameters, the smarter it is.

The number of model parameters is indeed related to expressiveness, but it does not grow linearly. As the number of parameters increases, the performance improvement will gradually flatten out, and there may even be an "over-parameterization" phenomenon, that is, the model is too complex, resulting in a decrease in generalization ability. For example, OpenAI's GPT-3 has 175 billion parameters, but on some tasks, the optimized GPT-2 (1.5 billion parameters) performs well. Meta's LLaMA model achieves comparable performance to GPT-3 with fewer parameters through a more efficient training method.

At the same time, as the number of model parameters increases, the model may become so complex that it begins to capture the noise in the data rather than the true patterns. This causes the model to perform well on the training data but poorly on unseen data (i.e., test data), a phenomenon known as overfitting.

Large models require huge computing resources for training and reasoning. This includes high-performance CPUs, GPUs, or TPUs, as well as large amounts of memory and storage space, and not all organizations or application scenarios can afford such resource consumption. In some cases, smaller models may be more flexible and easier to adapt to new data and tasks.

In practical applications, the selection of models often requires a trade-off in performance, resource consumption, training time, and other aspects. “Bigger is better” is not always the case.

2. Myth 2: Computing power input is positively correlated with model performance

Within a certain computing power range, increasing computing power investment can significantly improve model performance, because more computing power means that more data can be processed and more complex calculations can be performed, thereby training a more accurate model. However, when computing power investment reaches a certain level, its marginal effect will gradually weaken. In other words, if computing power investment continues to increase, the improvement in model performance may not be obvious, and there may even be diminishing returns. For example, training GPT-3 consumed thousands of GPUs, but subsequent studies found that by optimizing data quality and training methods, similar results can be achieved with less computing power.

In addition to computing power input, model performance is also affected by multiple factors such as data quality, model architecture, algorithm selection, and training strategy. If other factors are not optimized (such as high data quality noise and high repetition rate), simply increasing computing power input may not significantly improve model performance. For example, DeepMind's Chinchilla model research found that instead of blindly increasing computing power, it is better to balance the amount of data and the size of the model, so that better results can be achieved with the same computing power.

In practical applications, computing resources are usually limited and need to be allocated reasonably to maximize overall benefits. With the increase in computing costs, how to save computing costs while ensuring model effects has become one of the core concerns of enterprises and scientific research institutions. Simply increasing computing power investment may cause costs to soar, but the benefits may not match it. Therefore, it is necessary to comprehensively consider multiple factors to formulate a reasonable computing power investment strategy to maximize the model effect.

3. Myth 3: Big models have human-level understanding and reasoning capabilities

The big models are essentially statistical pattern matching tools that learn language rules through massive data, but they do not have the ability to truly "understand". Human understanding is based on rich background knowledge, emotional experience, intuition, and complex cognitive processes. We can not only understand the literal meaning, but also the context, metaphor, emotional color and other deep meanings. Although the big models can process large amounts of text data, identify patterns and generate responses, they are more based on statistical and probabilistic methods to match and predict inputs, rather than conducting in-depth semantic analysis and understanding like humans.

Human reasoning abilities include logical reasoning, inductive reasoning, deductive reasoning, and other types of reasoning, and are able to handle complex, abstract problems and think creatively. Large models have certain performance in logical reasoning, especially in specific fields and tasks. However, their reasoning abilities are usually based on statistical patterns in training data, rather than reasoning based on rules, principles, and concepts like humans do. In addition, the reasoning ability of large models may drop significantly when dealing with problems beyond their training scope.

The output of a large model depends heavily on its training data and training methods. If the training data is not comprehensive or representative enough, or if the training method is flawed, the accuracy of the model may be affected.

4. Myth 4: Big models are universal tools, suitable for all scenarios

Large models perform well on general tasks, but require targeted optimization in specific fields. Data in specific fields are often highly specialized and complex, which increases the difficulty of data labeling. Therefore, it is necessary to integrate professional knowledge into model training through cooperation with domain experts to improve the professionalism and accuracy of the model. If the data labeling is inaccurate or incomplete, it will directly affect the training effect and performance of the large model. In addition, data in specific fields may be relatively scarce, which limits the training scale and effect of large models.

In certain sensitive fields (such as medicine, law, etc.), model interpretability is crucial. Users need to understand the decision-making basis and reasoning process of the model to ensure the accuracy and reliability of its decisions. However, large models usually have complex structures and parameters, which makes them difficult to interpret in some scenarios.

5. Myth 5: Large models do not need to be continuously updated

Data in the real world is constantly changing, and the emergence of new words, expressions, or social phenomena may affect the model's ability to understand and predict. Regularly updating the model to incorporate new data can make it better adapt to the current language and social environment. At the same time, with the continuous advancement of technology, new algorithms and training methods continue to emerge, and these new technologies can often significantly improve the performance and efficiency of the model. Continuously updating the model allows it to take advantage of the latest technological achievements, thereby improving the accuracy of the model.

In addition, with the continuous improvement of laws and regulations and the enhancement of data protection awareness, models need to be continuously updated to adapt to new security standards and compliance requirements. This includes protecting user privacy, preventing data leakage, and ensuring the legality and ethics of model output. User needs and feedback are also an important driving force for continuous improvement of models. By collecting and analyzing user feedback, we can understand the performance and problems of the model in actual applications, so as to carry out targeted updates and optimizations.

Therefore, in order to keep the model accurate, adaptable and competitive, it is crucial to regularly update and optimize the model. This includes introducing new technologies, incorporating new data, solving performance issues, complying with security compliance requirements, and responding to user needs and feedback.

6. Summary

Although large language models have powerful text processing capabilities, the bigger the better, and computing power investment is not completely positively correlated with model performance. At the same time, large models do not have true understanding and reasoning capabilities, and are difficult to explain in some scenarios. More importantly, large models need to be continuously updated to adapt to changing data, technology, regulations, and user needs. Therefore, when applying large language models, it is necessary to comprehensively consider multiple factors and formulate reasonable strategies to maximize the model effect.

Author: Song Jingjing

Unit: China Mobile Research Institute