History, Development, and Principles of Large Language Models—An Introductory Survey

大型语言模型:历史、发展与原理概览
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本文提供大型语言模型(LLM)的概述,从历史、发展、原理到应用,揭示其在自然语言处理中的重要性。虽然LLM在多个领域展现巨大潜力,但对其理解的限制阻碍了其完全发挥作用。该调查旨在使广泛受众理解LLM的基础和未来趋势,强调其局限性并指出未来研究方向。

本文是LLM系列文章,针对《History, Development, and Principles of Large Language Models—An Introductory Survey》的翻译。

摘要

语言模型是自然语言处理(NLP)的基石,它利用数学方法来概括语言规律和知识,用于预测和生成。经过几十年的广泛研究,语言建模已经从最初的统计语言模型(SLM)发展到现代的大型语言模型(LLM)。值得注意的是,LLM的快速发展已经达到了处理、理解和生成人类级文本的能力。然而,尽管LLM在改善工作和个人生活方面具有显著优势,但一般的实践者对这些模式的背景和原则的了解有限,阻碍了它们的全部潜力。值得注意的是,大多数LLM审查侧重于特定方面,并使用专门的语言,这对缺乏相关背景知识的从业者构成了挑战。有鉴于此,本次调查旨在提供LLM的可理解概述,以帮助更广泛的受众。它努力通过探索语言模式的历史背景和追踪其随时间的演变来促进全面理解。该调查进一步调查了影响LLM发展的因素,强调了关键贡献。此外,它专注于阐明LLM的基本原理,为观众提供基本的理论知识。该调查还强调了现有工作的局限性,并指出了有希望的未来方向。

1 引言

2 大型语言模型的历史与发展

3 大型语言模型的原理

4 大语言模型的应用

5 大型语言模型的缺点和未来发展方向

6 结论

Language models have shown remarkable capabilities in predicting the effects of mutations on protein function without prior examples, a task known as zero-shot prediction. This ability is rooted in the way these models are trained and the vast amount of data they process. During training, language models learn to understand the context and relationships between different parts of a sequence. In the case of proteins, this means learning the relationships between amino acids and how changes in these sequences can affect the overall structure and function of the protein. By analyzing the co-occurrence patterns of amino acids across many protein sequences, language models can infer the importance of specific residues for maintaining the protein's function[^1]. When it comes to making predictions about mutations, language models can use the learned information to assess the likelihood that a particular mutation will disrupt the protein's function. This is done by evaluating the impact of the mutation on the local and global properties of the protein, such as its stability, folding, and interactions with other molecules. The model can then provide a score or probability indicating the effect of the mutation on the protein's function[^1]. One of the key advantages of using language models for zero-shot prediction is their ability to generalize from the data they have been trained on. Even without specific examples of certain mutations, the models can make educated guesses based on the general principles they have learned about protein sequences and structures. This makes them particularly useful for identifying potential disease-causing mutations or for guiding the design of new proteins with desired functions[^1]. For instance, a study demonstrated that a language model could predict the effects of mutations on the binding affinity of a protein to its ligand. The model was able to identify which mutations would lead to a decrease in binding affinity, even when those mutations had not been observed in the training data. This kind of prediction is crucial for understanding the molecular basis of genetic diseases and for developing targeted therapies[^1]. Here is a simplified example of how a language model might be used to predict the effects of mutations on protein function: ```python def predict_mutation_effect(model, wild_type_sequence, mutant_sequence): # Encode the sequences into a format suitable for the model encoded_wild_type = encode_sequence(wild_type_sequence) encoded_mutant = encode_sequence(mutant_sequence) # Get the model's predictions for both sequences wild_type_prediction = model.predict(encoded_wild_type) mutant_prediction = model.predict(encoded_mutant) # Calculate the difference in predictions to estimate the effect of the mutation effect = mutant_prediction - wild_type_prediction return effect ``` In this example, the `predict_mutation_effect` function takes a pre-trained model, a wild-type protein sequence, and a mutant sequence as inputs. It encodes the sequences into a format that the model can process, then uses the model to generate predictions for both sequences. The difference between these predictions is used to estimate the effect of the mutation on the protein's function. The application of language models in this domain is still an active area of research, and there are ongoing efforts to improve the accuracy and reliability of these predictions. Nevertheless, the current capabilities of language models represent a significant step forward in our ability to understand and manipulate protein function through computational means[^1].
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