Understanding Causal LLM’s, Masked LLM’s, and Seq2Seq: A Guide to Language Model Training Approaches

In the world of natural language processing (NLP), choosing the right training approach is crucial for the success of your language model. In this article, we’ll delve into the differences between Causal Language Modeling (CLM), Masked Language Modeling (MLM), and Sequence-to-Sequence (Seq2Seq) approaches, discussing their importance, and exploring best practices for using them.

Why it’s important to understand CLM, MLM, and Seq2Seq

Understanding these training methods allows you to select the most appropriate approach for your specific NLP task, ultimately enhancing your model’s performance. Each method has its unique strengths and weaknesses and is suited to different types of problems. By understanding the fundamentals of each approach, you can optimize your model’s training and fine-tuning, leading to better outcomes.

Causal Language Modeling (CLM)

CLM is an autoregressive method where the model is trained to predict the next token in a sequence given the previous tokens. CLM is used in models like GPT-2 and GPT-3 and is well-suited for tasks such as text generation and summarization. However, CLM models have unidirectional context, meaning they only consider the past and not the future context when generating predictions.

Masked Language Modeling (MLM)

MLM is a training method used in models like BERT, where some tokens in the input sequence are masked, and the model learns to predict the masked tokens based on the surrounding context. MLM has the advantage of bidirectional context, allowing the model to consider both past and future tokens when making predictions. This approach is especially useful for tasks like text classification, sentiment analysis, and named entity recognition.

Sequence-to-Sequence (Seq2Seq)

Seq2Seq models consist of an encoder-decoder architecture, where the encoder processes the input sequence and the decoder generates the output sequence. This approach is commonly used in tasks like machine translation, summarization, and question-answering. Seq2Seq models can handle more complex tasks that involve input-output transformations, making them versatile for a wide range of NLP tasks.

Key Differences in CLM, MLM, Seq2Seq

key differences in the implementation, architecture, and output models for causal language modeling (CLM), masked language modeling (MLM), and sequence-to-sequence (seq2seq) modeling.

Causal Language Modeling (CLM):

• Implementation: In CLM, the model is trained to predict the next token in the sequence, given the previous tokens. During training, the input tokens are fed into the model, and the model predicts the probability distribution of the next token. The loss is calculated based on the model’s predictions and the actual target tokens, which are just the input tokens shifted by one position.
• Architecture: CLM is typically used with autoregressive models like GPT. These models use a unidirectional (left-to-right) Transformer architecture, where each token can only attend to the tokens that come before it. This prevents the model from “cheating” by attending to the target tokens during training.
• Output Model: A fine-tuned CLM model can generate coherent text by predicting one token at a time, making it suitable for text generation tasks. However, it may not be as effective at capturing bidirectional context compared to MLM models.

Masked Language Modeling (MLM):

• Implementation: In MLM, the model is trained to predict masked tokens within the input sequence. During preprocessing, a certain percentage of tokens are randomly masked, and the model is trained to predict the original tokens at those masked positions. The loss is calculated based on the model’s predictions and the actual target tokens (the original tokens that were masked).
• Architecture: MLM is used with models like BERT, which use a bidirectional Transformer architecture. Unlike CLM models, MLM models can attend to all tokens in the input sequence during training, allowing them to capture context from both left and right.
• Output Model: A fine-tuned MLM model is better at understanding context and relationships between words in a sequence, making it suitable for tasks like text classification, sentiment analysis, named entity recognition, or question answering.

Sequence-to-Sequence (seq2seq) Modeling:

• Implementation: In seq2seq modeling, the model is trained to generate output sequences based on input sequences. The model consists of two parts: an encoder that encodes the input sequence into a latent representation, and a decoder that generates the output sequence based on this latent representation. The loss is calculated based on the model’s predictions and the actual target output tokens.
• Architecture: Seq2seq models typically use an encoder-decoder architecture, where both the encoder and decoder can be based on the Transformer architecture (e.g., T5, BART) or other architectures like LSTMs (e.g., the original seq2seq model). The encoder processes the input sequence and generates a context representation, while the decoder generates the output sequence based on the encoder’s output and its own hidden state.
• Output Model: A fine-tuned seq2seq model is better at tasks where the model needs to generate coherent output text based on input text, such as summarization, translation, or question answering.

In summary, the main differences between these approaches lie in their training objectives, model architectures, and the tasks they are suited for. CLM models focus on predicting the next token and are good for text generation tasks, MLM models focus on predicting masked tokens and are good for tasks requiring bidirectional context understanding, and seq2seq models focus on generating output sequences based on input sequences and are good for tasks requiring the generation of coherent output text.

MLM parameter in Huggingface selects MLM or CLM

The mlm parameter in the DataCollatorForLanguageModeling class is a boolean flag that indicates whether you want to train a masked language model (MLM) or a causal language model (CLM).

• mlm=True: The data collator will prepare the training data for masked language modeling. In this mode, the input sequences will have a certain percentage of tokens masked, and the model will be trained to predict the original tokens at those masked positions. This is the training objective used by models like BERT.
• mlm=False: The data collator will prepare the training data for causal language modeling, also known as autoregressive language modeling. In this mode, the model will be trained to predict the next token in the sequence given its previous tokens. This is the training objective used by models like GPT.

what are the drawbacks of MLM vs CLM for a LLM chatbot?

Each language modeling technique, Masked Language Modeling (MLM) and Causal Language Modeling (CLM), has its own advantages and drawbacks when used for building a chatbot. Here are some key points to consider:

Masked Language Modeling (MLM):

1. Pros:

• MLM can potentially capture bidirectional context, as the model learns to predict masked tokens based on both the preceding and following tokens. This can help the model understand the context more effectively, which might be useful in some chatbot scenarios.

1. Cons:

• MLM models like BERT are not designed to generate text autoregressively. While they can be fine-tuned for various NLP tasks, they are not inherently built for text generation like chatbot responses. Adapting MLM models for text generation typically requires additional architectural changes, such as adding a decoder or using a seq2seq model.
• During inference, MLM models cannot predict tokens incrementally, as the training involves predicting masked tokens in parallel. This may result in a less coherent chatbot response, as the model has not been trained to generate text sequentially.

Causal Language Modeling (CLM):

1. Pros:

• CLM models like GPT are designed for autoregressive text generation, making them more suitable for chatbot applications. They predict the next token in the sequence given the previous tokens, which aligns with how chatbot responses are generated.
• CLM models can generate coherent and contextually relevant responses because they are trained to predict the next token in a sequence based on the preceding tokens, taking into account the context provided by the input.

Cons:

• CLM models do not explicitly capture bidirectional context, as they only generate tokens based on the preceding tokens. This may lead to a slightly less nuanced understanding of context compared to MLM models, which consider both preceding and following tokens during training.
• Due to their autoregressive nature, CLM models may have slower inference times than MLM models, especially when generating long sequences, as they have to predict each token one at a time.

Which popular LLM’s were trained with DataCollatorForSeq2Seq collator?

DataCollatorForSeq2Seq is typically used for sequence-to-sequence (seq2seq) models, where the model is designed to generate output sequences based on input sequences. Some popular seq2seq models in the Hugging Face Transformers library include:

1. BART (Bidirectional and Auto-Regressive Transformers): BART is a denoising autoencoder with a seq2seq architecture, which has been shown to perform well on a variety of natural language understanding and generation tasks. It is pre-trained using a denoising objective, where the model learns to reconstruct the original text from corrupted versions of it.
2. T5 (Text-to-Text Transfer Transformer): T5 is a seq2seq model that frames all NLP tasks as text-to-text problems. It is pre-trained using a denoising objective similar to BART. T5 has been shown to perform well on various NLP tasks, including translation, summarization, and question-answering.
3. MarianMT: MarianMT is a seq2seq model specifically designed for neural machine translation. It is part of the Marian NMT framework and has been trained on translation tasks for various language pairs.
4. Pegasus (Pre-training with Extracted Gap-sentences for Abstractive Summarization): Pegasus is a seq2seq model designed for abstractive summarization. It is pre-trained using a gap-sentence generation task, where important sentences are removed from the input, and the model learns to generate these missing sentences.
5. ProphetNet: ProphetNet is a seq2seq model with a novel self-supervised objective called future n-gram prediction. It has been shown to perform well on tasks like abstractive summarization and question generation.

Conclusion

Understanding the differences between CLM, MLM, and Seq2Seq is crucial for selecting the most appropriate training approach for your language model. By following best practices and leveraging the strengths of each method, you can optimize your model’s performance and achieve better results in your NLP tasks.