Generalization and Zeros in NLP

Introduction

Introduction to Generalization in NLP

Generalization is the ability of an NLP model based on machine learning or statistical or deep learning models to learn and properly predict correctly the pattern of unseen data or applied to new, real-world data drawn from the same distribution as that of the training data from which it is trained on.

• Generalization is typically most closely related to two main factors, when a model is non-generalizable it could be associated with either overfitting or underfitting conditions.
  • There can be models that have learned the training dataset too well and know nothing else, it performs on the training dataset but does not perform well on any other new inputs. This is the case of overfitting and if it is managed well, the process of generalization becomes more achievable.
  • There will also be the case of models that are designed such that they do not understand the underlying problem statement and acts poorly on a training dataset and do not perform on new inputs. This is the case of underfitted models.
  • Good Fit Model is the one we need to target as the desired scenario as the model appropriately learns the training dataset and generalizes it to new inputs.

Overall, the goal of generalization in NLP is to build models that can accurately and reliably make predictions on new unseen data and can be applied to a wide range of real-world tasks and scenarios. Let us see a few ways to improve the nlp generalization power of models.

Introduction to Issue of Zeroes in NLP

The main issue of zeroes in natural language processing (NLP) techniques and particular NLP generalization is the conundrum that many of these NLP models rely on statistics and probabilities in learning from the patterns underlying the data to learn the parameters of the statistical or machine learning models.

• Example: If a word is present in all documents in a corpus, its inverse document frequency (IDF) will be zero and this can affect the overall weighting of that word in further calculations of modeling.
• The problem arises when dealing with rare words or events, and the probabilities have to be estimated as zero. This in turn affects the learning process leading to poor performance on new, unseen data as the model has no way of handling these rare words or events.

The zero issues can be solved by several techniques such as smoothing methods which aim to reduce the impact of zeroes on the model's performance. These techniques can help improve the model's ability to generalize to new data but it is still a difficult and ongoing area of research.

The Problem With State Of The Art NLP Models

State-of-the-art NLP models like any machine learning model struggle with a variety of problems and fail to generalize to new unseen data. This can be particularly challenging in the context of natural language where the number of possible sentences and word combinations are typically vast.

Of the various problems challenging nlp generalization, the most important to learn about is our data bias, the issue of passive learning, the issue of surface learning, issue of explainability and robustness. Let us look at these further and learn in-depth about them.

• Data bias: Most of the current state-of-the-art NLP models are often trained on large amounts of data and this data can reflect the biases of the people and sources that created it. Data bias is the case where datasets used for training NLP models or other purposes are unrepresentative of the real-world population leading to inaccurate or skewed results.
  • Data bias affects individuals in several ways depending on the specific situation it is used for. If machine learning models are trained on biased datasets, these biased models may make incorrect predictions or decisions about people from underrepresented groups leading to unfair treatment or discrimination such as a person being denied a loan or job opportunity based on the model's biased output.
  • Data bias also has more general effects such as perpetuating stereotypes and contributing to unequal societal outcomes. If a dataset used for crime prediction is biased against a particular group, it could result in that group being disproportionately targeted by law enforcement.
  • One important way to alleviate this bias is to make individuals consuming these models aware of the potential for data bias and to consider how it might affect them in different situations. It is also important for organizations and individuals who use data and NLP models to take steps to avoid and mitigate bias in their datasets and models.
• Passive Learning: Many of the current state-of-the-art language models even though trained on huge amounts of data and operated with billions of parameters are generating unnatural language because they are passive learners.
  • These state-of-the-art models read input and generate output accordingly, but unlike a human learner, they do not reflect on what they generated according to appropriate linguistic criteria such as relevance, style, repetition, and entailment.
  • Passive learning is a type of learning where the model receives a training signal but has no control over the data that it is being trained on. Passive learning is a major problem in nlp generalization and natural language processing (NLP) models because the model is not able to actively seek out and learn from the most relevant and informative examples in the data.
  • Example: If a model is passively trained on a dataset of text, it may not have the opportunity to ask questions or clarify any ambiguities in the data leading to a lack of understanding and poor performance on new and unseen data.
  • Solutions to alleviate passive learning issue: Research has been developing around active learning methods for NLP generalization which allow the model to actively seek out and select the most informative examples in the data to learn from.
    • This can help the model learn more effectively and improve its performance on new data.
    • The only caveat with implementing active learning in NLP is that it can be challenging and also an ongoing active area of research.
• Surface Learning: Surface learning is the issue where some natural language processing (NLP) state of the art models focus on learning the surface-level patterns in the data such as the specific words and phrases that appear in the text.
  • The issue with surface learners is that learning such patterns can be an effective way for the model to achieve high performance on the training data but it can also make the model brittle and prone to poor performance on new unseen data.
  • Surface learned models do not know real-world scenarios as humans understand them. They also do not capture the higher-order relationships among facts, entities, events, or activities, which for humans can be the key cues for language understanding.
  • Example: Imagine an NLP model that is trained on a dataset of text and learns to predict the sentiment of a sentence based on the specific words that appear in the sentence
    • The model may perform well on sentences that are similar to those in the training data.
    • But, if the model encounters a sentence with different word order or structure, it may not be able to apply its learned patterns and may produce the wrong output leading to poor nlp generalization.
    • Approaches to tackle the issue of surface learning: Recently research has been focussing on developing NLP models that go beyond surface-level patterns and learn more abstract and generalizable representations of language. These models can be more robust and better able to generalize to new data but developing and training them can be challenging.
• Issues of Explainability: Despite the success and adoption of state of the art based nlp models, most models based on machine learning and deep learning for text and nlp generalization are complex, black boxes, and the outputs are not easily explainable during the learning and the prediction phases.
  • This lack of explainability can make it difficult to trust and use these models in certain applications, especially in fields where the output of the decision from these models needs high reliability and trust like medical and legal.
• Issue of Robustne: Many times, state of the art NLP models can be fragile and easily fooled by adversarial attacks using carefully constructed inputs, the goal of which is to deceive the model into giving away sensitive information, making incorrect predictions, or corrupting them. Adversarial attacks are broadly classified using two types - Black box or white box & targeted vs non-targeted attacks.
  • White-box vs black-box attacks: In the white box kind of attacks, the attackers have access to the initial parameters of the model like the architecture, loss function, or the training data of the model. The adversary does not have any information about the targeted model in black box attacks.
  • Targeted vs. Non-Targeted: This is mostly by the method of creating adversarial images. In non-targeted attacks, the adversary assigns the adversarial image to any class regardless of the class of the true image. Adversary assigns the adversarial image to a specific class in targeted attacks.
  • Example: Models trained to classify the sentiment of a sentence may produce the wrong output if the sentence includes irony or sarcasm. This fragility can limit the real-world applicability of these models.

What is Generalization?

Generalization in the context of machine learning models refers to the model's ability to perform well on unseen data rather than just on the data it was trained on.

Various factors affect the generalization ability of NLP models like model parameters, quality of data, presence of vocabulaout-of-vocabularyion approaches used in assessing the model, and how models are optimized. Let us learn in depth about these aspects further.

Effect of Model Parameters on NLP Generalization

The choice of the model architecture and hyperparameters can also have a major impact on the model's ability to generalize.

• Issue of Overfitting: - Using a more complex model with a large number of parameters may result in overfitting and poor generalization performance while a simpler model may be more prone to underfitting but may have better generalization performance.
• On the contrary the other hand, the use of carefully selected large-scale, pre-trained language models such as BERT and GPT-3 as a first step input to other models can also improve generalization performance by providing the model with a strong starting point and a large amount of knowledge about the problem at hand.

Some NLP models even with reasonable parameters suffer from overfitting due to training on a small or highly specific dataset so it learns to make predictions based on patterns in the training data that may not generalize to other data.

Effect of Data Quality on NLP Generalization

Third, NLP models can be sensitive to the quality and formatting of the input data. For example, a model trained on well-formatted, grammatically correct text may not perform well on noisy or unstructured data.

• Effect of data on nlp generalization ability of models: The data used to train a model is a crucial factor in its ability to generalize and it is important to carefully consider the quality and diversity of the data when building and training a model.
  • Models trained on a diverse and representative dataset have a better ability to learn generalizable patterns and features from the data.
  • Example: If a model is trained on a dataset that only contains data from a specific geographic region or period, it may not be able to accurately make predictions on data from other regions or periods.
  • The amount of data used to train a model can also impact its generalization ability. A model in general trained on a larger dataset is more likely to be able to learn more robust and generalizable patterns from the data, which will improve its ability to make accurate predictions on unseen data.
• Issue of OOV of Vocabulary words: Most NLP models can struggle with out-of-vocabulary (OOV) words which are words that are not present in the model's training data. When the model encounters an OOV word, it may be unable to make a prediction and may produce incorrect or inconsistent results.

Effect of Evaluation on NLP Generalization

There are a lot of challenges associated with datasets and evaluation in natural language processing (NLP) models such as the lack of large, diverse, and high-quality datasets for many NLP tasks which can make it difficult for models to learn effectively and generalize to new data.

• Lack of standardized evaluation metrics and benchmarks for many NLP tasks also makes it difficult to compare different models and assess their performance
• The high dimensionality and complexity of natural language data also make it difficult to design and interpret experiments leading to issues of overfitting and bias in the evaluation of models.
• We can tackle these issues using techniques such as data augmentation, transfer learning, and active learning to improve the quality of datasets and evaluations, and by developing better metrics and benchmarks separately to assess the performance of NLP models resulting in better nlp generalization.

Effect of Optimization Behavior

The generalization performance of the natural language processing (NLP) model can also be understood from the perspective of optimization behavior by analyzing how the model's parameters are updated during training.

• Learning process in NLP models: The goal of the training and learning process in NLP models is to find a set of parameters that minimize the difference between the model's predictions and the ground truth labels in the training data.
  • The process is typically performed using some form of stochastic gradient descent where the model's parameters are updated based on the gradient of the loss function concerning the parameters.
  • The generalization performance of a model can be understood by analyzing how the parameters are updated during training and how these updates affect the model's ability to make accurate predictions on unseen data.
• Effect of the learning process on generalization: If the model's parameters are updated in a way that is too heavily influenced by the specific details of the training data or learning algorithm (like learning rate for example), it may not generalize well to new data.
  • If the updates are more regularized and aim to learn more general-purpose representations, the model is more likely to generalize well.

By understanding the optimization behavior of an NLP model, it is possible to gain insights into its generalization performance and make improvements as needed.

Ways to Improve Generalization in NLP

To address the issues around the state-of-the-art models and improve the generalization ability of NLP models, we can incorporate a multitude of techniques like incorporate inductive biases, regularization, data augmentation, transfer learning, domain adaptation, incorporating common sense into models, embodied learning, etc. Let us learn about these techniques further.

Incorporating Inductive Biases into Model Learning

Inductive biases are assumptions that a model makes about the structure of the data it is trained on. These assumptions can help a model to learn more efficiently by guiding it towards solutions that are more likely to be correct given the data it has seen so far.

• There is a general debate in the nlp research community around whether inductive biases which are the set of assumptions used to learn a mapping function from input to output should be reduced or increased when learning the models.
• While designing the functional form of the model around what innate priors we should build into deep learning architectures, one school of thought argues that structural bias is necessary for learning from fewer data and high-order reasoning.
• An opposing viewpoint describes the structure as a necessary evil that forces us to make certain assumptions that might be limiting. The support for a reduction in inductive biases is argued around the fact that modern models that use linguistic-oriented biases do not result in the best performance for many benchmark tasks.
• Even though there is strong support for inducing linguistic structures in neural architectures within some sections of the research community, most of the models that induced structures seem to not work as expected in practice.
• Application to NLP models: Inductive biases can be incorporated into nlp models by using certain types of architectures, loss functions, or regularization techniques.
  • Example: A model might be designed to make assumptions about the syntactic structure of natural language sentences or the statistical properties of words and phrases in a corpus of text.
  • The model can learn more effectively and make more accurate predictions on unseen data by incorporating these assumptions.

Regularization

Regularization is a common technique that can be used to prevent overfitting in general where the model performs well on the training data but poorly on new data. Regularization methods introduce an additional term to the model's loss function so that the model can learn more generalizable representations of the data.

• Structured sparsity: This is a regularization technique used in nlp generalization to improve the efficiency and generalizability of a model.
  • The basic idea for structured sparsity is to design machine learning models to learn only the most important parameters for a given task and set the remaining parameters to zero or close to zero.
  • This can help to prevent overfitting and improve the interpretability and nlp generalization of a model by reducing the number of parameters that need to be learned and estimated.
  • Application of structured sparsity to NLP models: Structured sparsity can be applied to NLP models by using sparsity-inducing regularization terms in the loss function or by using structured pruning techniques to remove unimportant parameters from a trained model.
    • By using structured sparsity, NLP models can be made more efficient and interpretable, while still retaining their predictive power.

Regularization techniques such as dropout or weight decay can be used to prevent overfitting and improve the generalizability of a model.

Data Augmentation

• Data augmentation is a technique that involves generating additional training data by applying random transformations to the existing training data. This can help the model learn more robust and generalizable representations of the data and can improve the model's performance on new data.
• NLP example: We can generate new sentences by combining or splitting existing ones. "The dog sat on the mat" and "The cat slept in the bed" could be combined to create a new sentence "The dog sat on the mat, while the cat slept in the bed."
• Computer vision example: We can apply random transformations to existing images to generate new images. An image can be rotated, flipped, or cropped to create a new version of the same image.

Transfer Learning

• Transfer learning is a technique that involves the use of knowledge and representations learned by a pre-trained model on one task and applying them to a different but related task. This can help the model learn more efficiently and improve its performance on the new task.
• Another approach is to use multi-task learning that is distinct from transfer learning where a single model is trained to perform multiple related tasks simultaneously.
• With multi-task learning, the models can learn more general-purpose representations that apply to a wider range of tasks.

Domain Adaptation

• Domain adaptation is a technique used in nlp generalization to adapt models trained on one type of data called the source domain to a different type of data under the target domain.
• This kind of cross-learning is useful because real-world data can be highly diverse and unpredictable and a model that only works well on the specific data it was trained on is unlikely to be useful in practice.
• By using domain adaptation, a model can be made more flexible and capable of performing well on a wider range of data.

Incorporating Common Sense Into Models

Although most states of the art nlp models are effective in tasks most NLP like NER, sentiment analysis, etc., they still lack common sense and often produce nonsensical or unrealistic responses when given open-ended prompts. Some examples of these models are GTP-3, and BERT which are large-scale language models trained on a massive amount of text data and can generate human-like text.

• Incorporating common sense into these models is still a challenging but important task as common sense knowledge is essential for understanding and generating natural language.
• One approach is to use a large, manually constructed knowledge base such as WordNet or ConceptNet which encode common sense knowledge in the form of semantic relationships between words and concepts.
• One other approach is to use unsupervised learning techniques to automatically learn common sense knowledge from large amounts of text data.
• Example: A model might be trained to identify and classify common sense relations between entities mentioned in a text such as the location of an object or the intention of a speaker.

Embodied Learning

• Embodied learning is a learning technique that focuses on the interaction between an intelligent agent and its environment rather than just the internal representation of knowledge.
• Example use of embodied learning for NLP generalization: An NLP model might be trained to perform a language-related task such as machine translation or text summarization in a simulated environment, where it can interact with other agents and objects in the environment to learn about the task.
• Embodied learning approach can allow the model to learn more effectively by grounding its knowledge in real-world experiences rather than just through the internal processing of text data.

Zeros in NLP

One of the main problems with the presence of a large number of zeroes in the training data for a natural language processing (NLP) model is the negative impact it will have on the nlp generalization in its performance.

• One reason for this is that many NLP models rely on word counts or frequency to make predictions and a large number of zeroes in the data can distort these counts and frequencies leading to less accurate predictions.
  • If the probability of any word in the test set is 0, the entire probability of the test set is 0.
  • As per the definition of perplexity which is a general metric used to assess model performance, we can’t compute perplexity at all, since we can’t divide by 0.
• Having a large number of zeroes in the training data can cause the model to overfit the data making the model less useful in real-world applications.
  • Underestimating the probability of words from different places may occur and this will hurt the performance of any downstream applications we want to run on the models and data.
  • One of the easy-to-handle these issues are to pre-process the training data and remove any extraneous zeroes before training an NLP model but it may not be possible in all cases as we may have to discard many samples in such cases.

Zeros: Things like words or entities that don’t ever occur in the training set but do occur in the test set.

Causes for Zeroes in NLP

• Data Sparsity: Data sparsity refers to the phenomenon of having limited training data available for a particular task or language. This can make it difficult for NLP models to learn effective representations of the data and can lead to poor performance on the target task.
• Probability sparsity: This refers to the scenario of having very low probabilities for some events or outcomes in a probabilistic model. This also makes it difficult for a model to learn effectively and can lead to poor performance on the target task.

Ways to Tackle Sparsity in NLP Models

• We can also use larger and more diverse training datasets to help alleviate data sparsity and improve the performance of models in nlp generalization.
• In both cases of data and probability sparsity, sparsity can be addressed by using regularization techniques such as structured sparsity or dropout to prevent overfitting and improve the generalizability of the model.
• Model Sparsity: Model sparsity refers to the property of having many zero or near zero parameters in a model. Having sparsity in parameters is beneficial for nlp models as it can make a model more efficient and interpretable by reducing the number of parameters that need to be learned and estimated.
  • Model sparsity can be achieved through the use of some regularization techniques such as structured sparsity or pruning which encourage a model to learn only the most important parameters for a given task.
  • Also using certain model architectures such as sparse coding or low-rank decompositions can also promote sparsity naturally.

By using model sparsity NLP models can be made more efficient and interpretable while still retaining their predictive power.

Zero Probability Bigrams

Bigrams with zero probability: In NLP models especially n-gram language models, if a model is trained on a dataset of text and encounters a bigram (a sequence of two words) that it has never seen before or if an unknown word comes in the sentence then the probability for the bigrams becomes 0.

• This can lead to poor performance on new, unseen data because the model has no way of handling these rare bigrams.
• This problem of zero probability can be solved using smoothing methods like Laplace smoothing or Good turing discounting.