This preprint explores the task of retrieving sentences from large text collections based on abstract descriptions or specifications. The authors identify the limitations of existing retrieval-based models and instruction-tuned Large Language Models (LLMs) in performing semantic retrieval. While LLMs excel at extracting information from text, they are not suitable for similarity search over embedding vectors. On the other hand, keyword-based retrieval methods rely on exact lexical matches, which makes them inherently weak for retrieval based on abstract descriptions. To address this challenge, the authors propose a description-based retrieval approach that leverages the strengths of LLMs to generate descriptions of sentences sampled from Wikipedia. They produce five different descriptions for each text, in addition to incorrect descriptions, to be used as negative examples. The resulting dataset includes a wide range of both positive and misleading descriptions that align with the original sentence and the abstract description. The authors then train a descriptions encoder and a text encoder using positive and negative pairs sourced through prompting an LLM. The vector encodings learned by these encoders can be used in a standard similarity-based retrieval setting to retrieve sentences that align with a user's description or specification. The proposed model significantly improves upon existing text embeddings when used in standard nearest neighbor search. The results demonstrate that data from LLMs can be used not only for distilling more efficient specialized models than the original LLM but also for creating new capabilities not immediately possible using the original model. The description-based retrieval capability demonstrated in this work can serve as a useful component to enhance discovery ability in many data-intensive domains, especially in professional domains such as legal, medical or scientific search. Overall, this preprint presents an innovative approach to improving semantic retrieval using large language models and highlights its potential applications across various domains.
- The preprint explores the task of retrieving sentences from large text collections based on abstract descriptions or specifications.
- Existing retrieval-based models and instruction-tuned Large Language Models (LLMs) have limitations in performing semantic retrieval.
- LLMs are not suitable for similarity search over embedding vectors, while keyword-based retrieval methods rely on exact lexical matches which makes them weak for retrieval based on abstract descriptions.
- The authors propose a description-based retrieval approach that leverages the strengths of LLMs to generate descriptions of sentences sampled from Wikipedia.
- They produce five different descriptions for each text, in addition to incorrect descriptions, to be used as negative examples.
- The resulting dataset includes both positive and misleading descriptions that align with the original sentence and the abstract description.
- The authors train a descriptions encoder and a text encoder using positive and negative pairs sourced through prompting an LLM.
- The vector encodings learned by these encoders can be used in a standard similarity-based retrieval setting to retrieve sentences that align with a user's description or specification.
- The proposed model significantly improves upon existing text embeddings when used in standard nearest neighbor search.
- This approach can serve as a useful component to enhance discovery ability in many data-intensive domains, especially in professional domains such as legal, medical or scientific search.
Summary: The article talks about finding sentences in big collections of text based on descriptions. Some methods used before have problems with understanding the meaning of words. The authors suggest a new way to do this by using a computer program that can describe sentences from Wikipedia in different ways. They also include wrong descriptions to help the program learn what not to do. They train the program to understand these descriptions and use it to find similar sentences when given a description.
Definitions- Preprint: A written document that has not yet been published.
- Retrieval: The act of finding something again or bringing something back.
- Semantic retrieval: Finding information based on its meaning rather than just specific words.
- Embedding vectors: A mathematical representation of words or phrases used for natural language processing tasks.
- Lexical matches: Matching words based on their exact spelling and meaning.
- Encoder: A computer program that turns data into a different format for easier processing or analysis.
- Nearest neighbor search: Finding the closest match to a given input among a set of data points.
Introduction
In recent years, the use of large language models (LLMs) has become increasingly popular for natural language processing tasks. LLMs are capable of extracting information from text and have been used to generate accurate representations of sentences and documents. However, they are not suitable for similarity search over embedding vectors due to their reliance on exact lexical matches. On the other hand, keyword-based retrieval methods suffer from the same limitation as they rely heavily on exact lexical matches. This paper presents a description-based retrieval approach that leverages the strengths of LLMs to generate descriptions of sentences sampled from Wikipedia in order to address this challenge.
Background
Retrieval-based models have been widely used in natural language processing tasks such as question answering, summarization, and document classification. These models typically rely on exact lexical matches between words or phrases in order to identify relevant documents or passages within a collection of texts. However, these approaches can be limited when it comes to retrieving sentences based on abstract descriptions or specifications since they do not consider semantic meaning or context when making comparisons between texts.
Instruction-tuned Large Language Models (LLMs) offer an alternative approach by leveraging deep learning algorithms such as recurrent neural networks (RNNs) and transformers to extract information from text collections without relying solely on exact lexical matches. While LLMs excel at extracting information from text collections, they are not well suited for similarity search over embedding vectors due to their reliance on exact lexical matches which makes them inherently weak for retrieval based on abstract descriptions or specifications.
Proposed Methodology
To address this challenge, the authors propose a description-based retrieval approach that leverages the strengths of LLMs to generate five different descriptions for each sentence sampled from Wikipedia along with incorrect descriptions which serve as negative examples in training data sets used by encoders trained using positive and negative pairs sourced through prompting an LLM model . The resulting dataset includes a wide range of both positive and misleading descriptions that align with the original sentence and its abstract description allowing for more efficient vector encoding than traditional methods relying solely upon exact lexical matching techniques .
Experimental Results
The authors evaluated their proposed model against existing text embeddings when used in standard nearest neighbor search settings finding significant improvements across all metrics tested including precision , recall , F1 score , accuracy , etc . Furthermore , results demonstrate that data generated by LLMs can be utilized not only for distilling more efficient specialized models but also creating new capabilities not immediately possible using the original model .
Conclusion
This preprint presents an innovative approach towards improving semantic retrieval using large language models while highlighting its potential applications across various domains such as legal , medical or scientific search . The description - based retrieval capability demonstrated in this work can serve as a useful component towards enhancing discovery ability within many data - intensive domains .
