Sentence Completion Language Models: Enhancing Human Experience | Dhruv Matani | September 2023

Introduction:

In this tutorial, we will learn how to build a simple LSTM model for language modeling. We will discuss the concept of tokenization and explore different ways to represent tokens. We will also dive into sub-word tokenization and its advantages. Additionally, we will explore how to choose the vocabulary size for the model and discuss the layers involved in the PyTorch model. We will interpret the accuracy and loss metrics of the model. Finally, we will see how we can use the model to improve suggestions from a swipe keyboard and compute the probability of a word being a valid completion of a sentence prefix.

Full Article: Sentence Completion Language Models: Enhancing Human Experience | Dhruv Matani | September 2023

Building an LSTM Model to Predict Tokens: A Simple Guide

Tokenization and the Need for Context

In the world of language models, tokens play a crucial role. But what exactly is a token? Well, it can refer to different things depending on the context.

A token can be:
1. A single character or byte.
2. An entire word in the target language.
3. Something that falls between a single character and an entire word.

However, mapping a single character to a token can be limiting because it carries a lot of context about its occurrence. For example, the character “c” appears in various words, and predicting the next character after encountering “c” requires understanding the surrounding context.

On the other hand, mapping a single word to a token is not ideal either. Considering the vast number of words in English and the potential addition of new words, it becomes challenging to train a model that accounts for every word.

Sub-Word Tokenization: The Industry Standard

In 2023, sub-word tokenization is considered the industry standard. This approach assigns unique tokens to frequently occurring substrings of bytes. Typically, language models have a few thousand to tens of thousands of unique tokens. The BPE (Byte Pair Encoding) algorithm determines the criteria for identifying tokens.

Choosing the Vocabulary Size: Striking a Balance

When determining the vocabulary size (i.e., the number of unique tokens), striking a balance is crucial. Using too few tokens results in a token per character scenario, which makes it difficult for the model to learn meaningful patterns. On the other hand, using too many tokens results in embedding tables that overshadow the rest of the model’s weight, making deployment challenging in resource-constrained environments.

To strike this balance, we choose a vocabulary size of 6600 tokens. This allows us to train our tokenizer efficiently.

Unveiling the PyTorch Model

The PyTorch model we’ll be using is fairly straightforward and consists of the following layers:

1. Token Embedding: With a vocabulary size of 6600 and an embedding dimension of 512, this layer takes up about 15MiB of memory.
2. LSTM (Long Short-Term Memory): This layer has one hidden dimension of 786 and one layer. It occupies approximately 16MiB of memory.
3. Multi-Layer Perceptron: This layer has dimensions of 786, 3144, and 6600, respectively, totaling around 93MiB of memory.

In total, the complete model comprises about 31M trainable parameters, amounting to a size of approximately 120MiB.

Evaluating Model Performance: Accuracy and Loss

After training our model on 12M English language sentences using a P100 GPU for approximately 8 hours, we observed the following results:

– Loss: 4.03
– Top-1 Accuracy: 29%
– Top-5 Accuracy: 49%

It’s important to note that while the top-1 and top-5 accuracies may not seem impressive, they are not the most critical metrics for our specific problem. Rather, our primary goal is to have the model select the most syntactically and semantically coherent candidate word to complete a given sentence.

Using the Model to Improve Swipe Keyboard Suggestions

Let’s dive into an example to illustrate how our model can enhance swipe keyboard suggestions. Imagine that we have the partial sentence “I think,” and the user performs a swipe pattern starting at “o,” moving between the letters “c” and “v,” and ending between the letters “e” and “v.”

Based on this swipe pattern, some possible words that could be represented are “Over,” “Oct” (short for “October”), “Ice,” and “I’ve” (with the apostrophe implied).

By feeding these suggestions into our model, we get the following probabilities:

– [I think] [I’ve] = 0.00087
– [I think] [over] = 0.00051
– [I think] [ice] = 0.00001
– [I think] [Oct] = 0.00000

In this case, “I’ve” receives the highest probability, making it the most likely word to follow the sentence prefix “I think.”

Computing Next-Word Probability

To compute the probability that a word is a valid completion of a sentence prefix, we run the model in eval (inference) mode and provide the tokenized sentence prefix. To ensure consistency with the HuggingFace Tokenizers’ tokenization strategy, we also tokenize the candidate word by adding a whitespace prefix.

Assuming the candidate word consists of three tokens (T0, T1, and T2), we follow these steps:

1. Run the model with the original tokenized sentence prefix and check the probability of predicting token T0.
2. Add this probability to the “probs” list.
3. Run a prediction on the prefix + T0 and check the probability of token T1. Add this probability to the “probs” list.
4. Run a prediction on the prefix + T0 + T1 and check the probability of token T2. Add this probability to the “probs” list.
5. Multiply the individual probabilities in the “probs” list to obtain the combined probability of the candidate word being a completion of the sentence prefix.

In summary, through the effective utilization of our LSTM model and its tokenization capabilities, we can significantly enhance the accuracy and coherence of suggestions provided by swipe keyboards.

Summary: Sentence Completion Language Models: Enhancing Human Experience | Dhruv Matani | September 2023

In this article, the author discusses the process of building a simple LSTM model for language prediction. They explain the concept of tokenization and the challenges of mapping characters or words to tokens. They also highlight the importance of choosing an appropriate vocabulary size. The article includes code snippets and provides insights into model accuracy and loss interpretation. Additionally, the author demonstrates how the model can be used to improve suggestions from swipe keyboards by computing next-word probabilities.

Frequently Asked Questions

What are Language Models for Sentence Completion?

Language Models for Sentence Completion are algorithms or systems that are designed to generate the most probable completion for an incomplete sentence by predicting the missing words or phrases based on a given context.

How do Language Models for Sentence Completion work?

Language models use statistical techniques and neural networks to learn patterns and relationships from a large amount of text data. They consider the context of the sentence and analyze the co-occurrence of words to predict the most likely completion for a given incomplete sentence.

What are the applications of Language Models for Sentence Completion?

Language Models for Sentence Completion have various applications, including autocomplete suggestions in search engines, text prediction in chatbots, machine translation, speech recognition, and natural language understanding tasks.

What are some popular language models used for sentence completion?

Some popular language models used for sentence completion include OpenAI’s GPT (Generative Pre-trained Transformer), Google’s BERT (Bidirectional Encoder Representations from Transformers), and Microsoft’s DeBERTa (Decoding-enhanced BERT with Disentangled Attention).

How accurate are Language Models for Sentence Completion?

The accuracy of language models for sentence completion depends on various factors such as the amount and quality of training data, model architecture, and fine-tuning techniques. State-of-the-art models can achieve high accuracy, but there is always room for improvement.

Can Language Models for Sentence Completion understand context and semantics?

Language models can understand context to some extent by considering the surrounding words and phrases. However, they might struggle with understanding complex semantics and deeper meanings, as they primarily rely on statistical analysis.

Are Language Models for Sentence Completion always perfect?

No, language models are not always perfect. They can sometimes generate incorrect or nonsensical completions, especially when faced with ambiguous or uncommon sentence structures or when the training data is biased or limited.

Can Language Models for Sentence Completion be fine-tuned?

Yes, language models can be fine-tuned on specific tasks or domains to improve their performance. Fine-tuning involves training the model on a smaller dataset that is specific to the desired task, which helps in adapting the model to specific language patterns and requirements.

What are the limitations of Language Models for Sentence Completion?

Some limitations of language models include the potential for biased or offensive outputs, inability to understand nuances and sarcasm, and the risk of generating misleading or harmful content if not properly supervised or controlled.

Conclusion

Language Models for Sentence Completion play a crucial role in various natural language processing tasks and have seen significant advancements in recent years. While they have their limitations, continuous research and improvements in model architectures and training techniques are enhancing their accuracy and effectiveness.

Disclaimer

The FAQs provided here are for informational purposes only and are not an exhaustive representation of the topic. For more detailed information, it is recommended to refer to the original research paper or consult experts in the field.