You're all that I been looking for: A theoretical paradigm for Logically Pretraining Question-Answering models
Introduction:
This article attempts to draw attention to a potential learning procedure of language models, specifically for the task of Question-Answering, modeling the logical relationship between a given sentence, that we understand to be query, and an additional sentence that is provides the relevant response to the query. I propose that we can accomplish this by applying a linear transformation to a query context vector and subsequently get the correct response vector to search for some of its identical properties, within the query vector. The intuition behind this approach is that when the response vector searches for its properties, inside the transformation vector, it will also begin to search for the properties inside of the original query. Thus, we can imagine the link between all three vectors as some kind of chain that the model learns to view as some vector space, using a variation of the nearest neighbor algorithm. Although, this algorithm would theoretically work with any arbitrary natural language processing technique, the use of the attention mechanisms within transformers, specifically the sentence transformer, allows the model to learn the sematic similarities between each Query-Response vector. The outline of the blogpost continues as follows:(1) explaining the process of the sentence transformer. (2) Suggesting the Canonical correlation analysis algorithm. (3) Provide a supervised learning approach. (4) Provide an unsupervised learning approach. Feel free to check out the corresponding code for this proposal, that offers a concrete example of CCAs ability to navigate through text: Portfolio/An_rudimentary_example_of_CCA_for_Question_Anwering.ipynb at main · Ejeat12/Portfolio (github.com)
Sentence Transformers
Sentence Transformers is an architecture that aims to generate dense vector representations (embeddings) for sentences or text passages. These embeddings are designed to capture semantic similarities and contextual information of the input text. At a high level, Sentence Transformers employ a transformer model, such as BERT, as a backbone. To adapt the pre-trained transformer model for sentence-level embeddings, Sentence Transformers utilize a pooling strategy. This strategy aggregates the token-level representations into a fixed-length vector representation that represents the entire sentence. Common pooling methods include mean pooling, max pooling, and hierarchical pooling. After the pooling layer, Sentence Transformers often apply additional transformations, such as fully connected layers or non-linear activation functions, to refine the sentence embeddings further. These additional layers can be trained in a supervised or unsupervised manner, depending on the specific objective of the application. Regarding computational costs, Sentence Transformers can be computationally expensive due to the underlying transformer model. Transformers typically involve self-attention mechanisms that require processing each token in relation to all other tokens, resulting in a quadratic time complexity with respect to the input sequence length. This can be especially demanding for longer sentences or text passages. However, there are strategies to mitigate these costs. For example, Sentence Transformers can process sentences in batches, which allows for parallelization and improves computational efficiency. Additionally, pre-trained models can be fine-tuned on specific downstream tasks, which helps to balance the computational cost with improved performance on the desired objective. Overall, while Sentence Transformers can be computationally expensive, they offer powerful capabilities for generating informative sentence embeddings. The computational costs need to be carefully considered in applications where efficiency is crucial, but the benefits in terms of semantic understanding and contextual representation can outweigh the computational overhead in many use cases.
Canonical Correlation Analysis(CCA)
Canonical Correlation Analysis (CCA) is a multivariate statistical technique used to analyze the relationships between two sets of variables. In the context of aligning similarity between text vectors, CCA can be used to measure the similarity or correlation between two sets of vectors representing different textual data. To apply CCA, we would first represent the text vectors of the query and response using the sentence transformers embeddings, as mentioned previously. These techniques map the textual data into vector representations, capturing semantic information. Once we have the vector representations, CCA analyzes the linear relationships between the two sets of vectors by maximizing the correlation between them. In this case, we would have one set of vectors representing the query and another set representing the response CCA aims to find a linear combination of these two sets that exhibits the highest correlation. By aligning the similarity between the two text vectors using CCA, we can assess how closely related they are in terms of their semantic meaning. The higher the correlation, the more similar the vectors are in terms of the underlying semantic information. CCA provides a quantitative measure of this similarity. Compared to a traditional question-answering approach, CCA offers a different perspective. In a question-answering approach, a pre-defined set of questions and answers are used to train a model to identify the appropriate answer given a question. However, this approach may not generalize well to different questions or contexts. On the other hand, CCA allows for a more flexible and data-driven approach. It does not rely on pre-defined questions and answers but instead focuses on finding correlations between vector representations. This means that CCA can be more effective in capturing the semantic similarity between different text vectors, even when the wording or structure of the sentences varies. In summary, CCA can be useful for aligning similarity between text vectors by measuring the correlation between two sets of vectors representing different textual data. Compared to a traditional question-answering approach, CCA offers a more flexible and data-driven method to capture semantic similarity, making it potentially more effective in cases where the specific questions and answers are not pre-defined.
Supervised
The proposed Supervised-Learning procedure would theoretically go as follows: (1) Collect a dataset containing pairs of query-context vectors and corresponding response vectors. (2) Transform the textual data into vector representations using sentence embeddings. (3) Use the collected dataset to train a CCA model. The CCA model learns to find a linear transformation that maximizes the correlation between the query-context vectors and the response vectors. This linear transformation aims to align the similarity between the query-context vectors and the response vectors. (4) Apply the learned linear transformation to a query-context vector to obtain a transformed vector representation. This transformed vector represents the query-context vector in a transformed space that captures similar properties to the response vector. (5) Use a variation of the nearest neighbor algorithm to find the response vector in the transformed space that is closest to the transformed query-context vector. This identifies the most similar response based on the transformed representations. The intuition behind this approach is that by using CCA to learn a linear transformation, the model can capture the shared properties or similarity between the query-context vectors and the response vectors. Applying this transformation to the query-context vector allows it to search for similar properties as the response vector. The nearest neighbor search in the transformed space then identifies the closest response vector, indicating the most suitable response for the given query-context. While this approach may provide a mechanism for aligning similarity between vectors, it is important to note that the effectiveness of the method would theoretically rely on the training data and the complexity of the relationship between the query-context vectors and the response vectors. Additionally, the success of the variation of the nearest neighbor algorithm would rely on the suitability of the chosen algorithm and its adaptation to the transformed space.
Unsupervised:
By applying CCA in an unsupervised manner, the algorithm discovers latent semantic relationships and similarities between the text vectors without relying on explicit labels. The correlation analysis and similarity search allow you to identify text vectors that are most similar to a given query vector. The proposed Unsupervised-Learning procedure would theoretically go as follows: (1) Collect a large corpus of text data and preprocess it by tokenizing, removing stop words, and applying any necessary text cleaning techniques. (2)
Transform the preprocessed text data into vector representations using sentence embeddings. These embeddings capture the semantic meaning and context of the text. (3)
Examine the obtained CCA results to analyze the correlations between the text vectors. CCA provides correlation coefficients, and you can interpret higher correlation values as indicating greater similarity between the corresponding text vectors. (4) Given a query text vector, you can perform a similarity search by comparing it to other text vectors using the learned correlations from CCA. This can be done by calculating similarity scores, such as cosine similarity, between the query vector and the other vectors in the dataset.
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