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This April 2024 paper explores how to effectively transform decoder-only Large Language Models (LLMs) into powerful text encoders for generating rich, contextualised text embeddings.
This transformation is done through a process called LLM2Vec.
LLM2Vec provides an effective method for transforming generic, decoder-only large language models into powerful text encoders that generate domain-specific embeddings.
This capability is important for industries that require nuanced understanding and processing of text specific to particular fields such as legal, medical, financial, or technical documents.
LLM2Vec modifies decoder-only LLMs using three main steps:
Enabling Bidirectional Attention: This step overcomes the inherent limitation of decoder-only models, which traditionally use causal attention that only allows each token to attend to previous tokens. By enabling bidirectional attention, each token can now attend to all other tokens in the sequence, improving the model's ability to generate more contextually rich embeddings.
Masked Next Token Prediction (MNTP): After enabling bidirectional attention, the next step involves adapting the model to effectively use this new capability through MNTP. This training technique combines aspects of masked language modeling and next token prediction, allowing the model to predict a token based on both previous and following context.
Unsupervised Learning
Definition: In unsupervised learning, the model is trained on data without labels. The goal is often to identify patterns or structures within the data without any guidance on what outcomes should be predicted.
Application in LLM2Vec: Unsupervised training, such as using SimCSE, involves training the model to learn effective text representations by maximising the similarity between differently augmented views of the same data while minimising similarity with other contrasting examples in the dataset. This is done without specific target labels indicating the correct answer, focusing purely on the data's inherent characteristics.
Supervised Learning
Definition: Supervised learning involves training a model on a labeled dataset, where each training example includes an input paired with a correct label (or outcome). The model learns to map inputs to outputs based on this data.
Application in LLM2Vec: Supervised aspects in training could involve fine-tuning the model on specific tasks where the outcomes are known. For example, using labeled datasets where the text embeddings are specifically optimised to perform well on predefined tasks like text classification or entity recognition based on labeled examples.
Key Differences in Context
Nature of Data: Unsupervised training does not require labels and works by extracting patterns directly from the data. Supervised training relies on labeled data to learn the mapping from inputs to outputs.
Objective: The training objective in unsupervised learning typically involves learning good data representations or clustering, whereas in supervised learning, it is about predicting the label correctly.
Use Cases: Unsupervised learning is often used for exploring data, dimensionality reduction, or initial learning of data representation. Supervised learning is used when specific tasks need to be performed, like classification or regression.
Dependency on Data Labelling: Supervised learning's effectiveness heavily depends on the quantity and quality of the labelling in the training data, which can be costly and time-consuming. Unsupervised learning, however, can leverage unlabelled data, which is more abundantly available.
The paper evaluates LLM2Vec on several NLP tasks, including word-level tasks like chunking, named-entity recognition, and part-of-speech tagging, and more complex sequence-level tasks using the Massive Text Embeddings Benchmark (MTEB).
LLM2Vec outperforms traditional encoder-only models in these tasks, setting new benchmarks for unsupervised performance on MTEB.
The performance scores of the LLM2Vec-transformed models on the Massive Text Embeddings Benchmark (MTEB) provide insights into the capabilities and areas of strength of each model variant (S-LLaMA-1.3B, LLaMA-2-7B, and Mistral-7B).
Here are the top five key points based on the provided scores:
Overall Superiority of Mistral-7B
Across nearly all tasks, Mistral-7B consistently scores the highest, indicating its superior ability to generate and use text embeddings.
Strength in Classification and Retrieval Tasks
All three models demonstrate strong performance in various classification and retrieval tasks. Notably, Mistral-7B excels in classifying intent in contextual queries, which is critical for applications in digital customer service and chatbots.
High Performance in Semantic Textual Similarity (STS) Tasks
The models perform well in semantic similarity tasks, which are crucial for applications involving text comparison such as document deduplication, information retrieval, and summarisation.
Variability Across Different Tasks
There is noticeable variability in performance across different task types and datasets. This highlights the models' adaptability and potential need for domain-specific tuning to achieve optimal performance across various NLP tasks.
The absolute numbers in the scores provided for the MTEB tasks represent the model's performance metrics on each specific task.
These scores are likely percentages representing the accuracy or a similar measure (such as F1-score, precision, recall, or a custom metric relevant to the task) that quantifies how well the model is performing against the benchmark's standards.
Accuracy: This is a straightforward measurement where the score indicates the percentage of instances that the model predicted correctly out of the total number of instances evaluated.
F1-Score: This is a harmonic mean of precision and recall and is often used in cases where the balance between precision and recall is critical, especially in datasets with uneven class distributions.
Precision and Recall: Precision measures the accuracy of the positive predictions made by the model, while recall measures the ability of the model to find all the relevant cases (true positives).
Each task might use one of these or other specialised metrics depending on its requirements and the nature of the data.
For instance, classification tasks might use accuracy or F1-score, while retrieval tasks might measure precision at a certain cut-off rank or mean average precision.
The scores thus provide a quantitative measure of how well the model handles the task's challenges, like understanding context, distinguishing between nuanced categories, or retrieving the most relevant documents.
A higher score indicates better performance, showing that the model's embeddings effectively capture and utilize the linguistic and semantic nuances necessary for the task.
Data and Parameter Efficiency: LLM2Vec is highlighted for its efficiency in terms of data use and minimal adaptation required, making it suitable for scenarios with limited computational resources.
Universal Text Encoding: The approach demonstrates that decoder-only LLMs can be converted into universal text encoders capable of handling a wide variety of NLP tasks effectively, challenging the traditional use of encoder-only or encoder-decoder models for generating text embeddings.
Contextualised Representations
By converting decoder-only models to use bidirectional attention, LLM2Vec allows the model to consider full contextual information from both prior and subsequent tokens, leading to richer text embeddings.
Efficiency
LLM2Vec is described as highly efficient in terms of data and computational resources. It does not require extensive retraining or fine-tuning on large labeled datasets, which is often a significant limitation in deploying advanced NLP models.
Versatility and Adaptability
The transformed models can be adapted to a wide range of NLP tasks and domains with minimal additional investment, making it an economical choice for businesses.
State-of-the-Art Performance
LLM2Vec has demonstrated superior performance on benchmarks like the Massive Text Embeddings Benchmark (MTEB), outperforming existing encoder-only models, which emphasizes its capability to deliver high-quality embeddings that can significantly enhance various NLP applications.
This research suggests a paradigm shift in how we use LLMs for text embeddings, proposing a method that leverages the inherent strengths of decoder-only models (like training efficiency and robustness) and transforms them into versatile tools for embedding generation.
The simplicity and effectiveness of LLM2Vec potentially lower the barriers for adopting advanced NLP technologies in resource-constrained settings and broaden the scope of applications for LLMs in industry and academia.
Unsupervised Contrastive Learning: The final step involves using an unsupervised learning approach called , where the model learns to pull closer the embeddings of the same text with different dropout masks, and push apart embeddings of different texts. This method helps in refining the sequence-level embeddings without needing labeled data.
