Unlocking the Power of Large Language Models: A Comprehensive Guide to Pre-training Tasks in Natural Language Processing

Photo by NASA on Unsplash

Pre-training tasks in Natural Language Processing (NLP) are designed to help models learn a wide range of language patterns and representations before they are fine-tuned on a specific downstream task like sentiment analysis, question-answering, or machine translation. These tasks leverage large corpora of text to teach models the syntax, semantics, and knowledge embedded in a language. Here is a detailed summary of various pre-training tasks:

Masked Language Modeling (MLM) [1]

• Description: In MLM, some percentage of the input tokens are randomly masked, and the model is trained to predict the original identity of the masked words based on their context. This encourages the model to learn a deep understanding of language context and word relationships.
• Example: The BERT (Bidirectional Encoder Representations from Transformers) model uses MLM as its pre-training task. Given a sentence, “The cat sat on the [MASK],” the model learns to predict that the [MASK] is “mat”.

Causal Language Modeling (CLM) [2]

• Description: CLM, also known as Autoregressive Language Modeling, involves predicting the next word in a sequence given the previous words. This is a unidirectional task that models the probability of a word given its predecessors.
• Example: GPT (Generative Pretrained Transformer) series uses CLM. If given a sequence “The quick brown fox jumps over the lazy”, the model predicts the next word as “dog”.

Permutation Language Modeling [3]

• Description: In this task, the order of words in a sentence is randomly permuted, and the model is trained to predict the original order. This encourages the model to learn about the structure of sentences and the dependencies between words.
• Example: XLNet uses permutation-based language modeling where a sentence might be permuted as “brown the quick jumps fox,” and the model learns to restore the original order.

Next Sentence Prediction (NSP) [1]

• Description: NSP involves training a model to predict whether two given sentences logically follow each other or not. This task helps models understand the relationship between sentences.
• Example: BERT uses NSP in conjunction with MLM, see Figure 1. If given the sentence pair “The cat sat on the mat.” and “It was very fluffy.”, the model learns that these two sentences are likely to be consequent.

Sentence Order Prediction (SOP) [4]

• Description: Similar to NSP, SOP requires the model to predict the correct order of two unordered sentences. This is meant to improve the model’s understanding of the coherence and flow of discourse.
• Example: ALBERT (A Lite BERT) employs SOP instead of NSP to improve the model’s sense of context.

Replaced Token Detection [5]

• Description: In this task, some tokens in the input text are replaced with a plausible but incorrect token by a small generator network. The model is then asked to detect which tokens have been replaced.
• Example: ELECTRA trains a discriminator model to distinguish between “real” and “fake” tokens, similar to a GAN (Generative Adversarial Network) setup.

Contrastive Learning [6]

• Description: Contrastive learning involves learning to distinguish between a correct sentence and its corrupted version. It is often used to learn sentence embeddings.
• Example: SimCSE (Simple Contrastive Learning of Sentence Embeddings) uses contrastive learning to learn powerful sentence representations by contrasting positive pairs of sentences with negative (dissimilar) ones.

Translation Language Modeling (TLM) [7]

• Description: TLM is an extension of MLM used in multilingual models, where the input is a concatenation of parallel sentences in different languages, and the model predicts masked tokens considering context from both languages.
• Example: In a multilingual BERT model, given the English-French sentence pair “The cat sat on the [MASK]. / Le chat était assis sur le [MASK].”, the model predicts “mat” and “tapis” for the respective masked tokens.

Entity Masking, Replacement, and Prediction [8]

• Description: This task specifically focuses on entities within text. Entities are masked or replaced, and models are trained to predict or generate the correct entity based on the context.
• Example: LUKE (Language Understanding with Knowledge-based Embeddings) uses entity-aware self-attention mechanisms to better understand and predict entities in the text.

Pre-training tasks have become an essential foundation in modern NLP, especially for Large Language Models (LLMs), allowing models to develop a rich understanding of language that can be transferred to a variety of downstream tasks. The choice of pre-training tasks often depends on the intended application of the model and the nature of the data available for pre-training.

Reference

[1]. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

[2]. Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. https://www.mikecaptain.com/resources/pdf/GPT-1.pdf

[3]. Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., & Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. Advances in neural information processing systems, 32.

[4]. Lan, Z., Chen, M., Goodman, S., Gimpel, K., Sharma, P., & Soricut, R. (2019). Albert: A lite bert for self-supervised learning of language representations. arXiv preprint arXiv:1909.11942.

[5]. Clark, K., Luong, M. T., Le, Q. V., & Manning, C. D. (2020). Electra: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555.

[6]. Gao, T., Yao, X., & Chen, D. (2021). Simcse: Simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821.

[7]. Lample, G., & Conneau, A. (2019). Cross-lingual language model pretraining. arXiv preprint arXiv:1901.07291.

[8]. Yamada, I., Asai, A., Shindo, H., Takeda, H., & Matsumoto, Y. (2020). Luke: deep contextualized entity representations with entity-aware self-attention. arXiv preprint arXiv:2010.01057.