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LLM-Augmented Retrieval: Enhancing Retrieval Models Through Language Models and Doc-Level Embedding


Core Concepts
A model-agnostic doc-level embedding framework that leverages large language models (LLMs) to enrich the contextual information in document embeddings, significantly improving the effectiveness of widely-used retriever models.
Abstract
The paper introduces a novel LLM-augmented retrieval framework that enhances the performance of existing retrieval models. The key components are: Synthetic Relevant Queries: LLMs are used to generate synthetic queries that express the semantic of the original document from different angles, helping to match relevant queries to the document. Title: The title of a document is incorporated into the doc-level embedding, as it provides important context and keywords. Chunks (Passages): Long documents are divided into smaller chunks or passages, which are then combined with the synthetic queries and title to form the doc-level embedding. The doc-level embedding is designed to be model-agnostic and can be applied to both bi-encoder and late-interaction retrieval models. Experiments on the LoTTE and BEIR datasets show that the LLM-augmented retrieval framework significantly boosts the performance of Bi-encoders (Contriever and DRAGON) and reduces the performance gap for token-level late-interaction models (ColBERTv2) compared to the vanilla models. The paper also proposes improvements to key components of the retrieval model training process, such as adaptive negative sampling and a margin ranking loss function, further enhancing the effectiveness of the retrieval models.
Stats
The paper does not provide specific numerical data points, but rather focuses on the overall performance improvements achieved through the LLM-augmented retrieval framework.
Quotes
"We propose LLM-augmented retrieval, a model-agnostic framework that enriches the contextual information in the vector embedding of documents to improve the quality and robustness of existing retrievers." "We propose doc-level embedding, which combines more contextual information in the context embedding." "We evaluate this framework across different models and a wide range of datasets, establishing state-of-art quality beyond original models."

Key Insights Distilled From

by Mingrui Wu,S... at arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05825.pdf
LLM-Augmented Retrieval

Deeper Inquiries

How can the LLM-augmented retrieval framework be further extended to incorporate additional contextual information beyond synthetic queries, titles, and passages?

The LLM-augmented retrieval framework can be extended to incorporate additional contextual information by considering various aspects of the document that can enhance the understanding of its relevance to a query. One way to achieve this is by integrating metadata associated with the document, such as publication date, author information, or domain-specific tags. This metadata can provide valuable context that can influence the relevance of the document to a particular query. Furthermore, incorporating user behavior data, such as click-through rates or dwell times on documents, can offer insights into the perceived relevance of the document to users. By leveraging this information, the retrieval framework can better understand the user's intent and preferences, leading to more accurate retrieval results. Additionally, sentiment analysis and entity recognition techniques can be applied to extract emotional tone and key entities from the document. This information can help in capturing the nuances of the content and its potential relevance to specific queries. By integrating these additional contextual cues into the doc-level embedding process, the LLM-augmented retrieval framework can create more comprehensive representations of documents, enhancing the retrieval model's ability to match documents with user queries effectively.

How can the potential drawbacks or limitations of relying on LLMs for data augmentation be mitigated?

While LLMs offer powerful capabilities for data augmentation, there are potential drawbacks and limitations that need to be addressed to ensure the effectiveness and reliability of the retrieval framework. Some of these limitations include the risk of hallucination, computational resource requirements, and the need for domain-specific fine-tuning. To mitigate the risk of hallucination, where LLMs generate inaccurate or misleading information, it is essential to implement robust validation and filtering mechanisms. This can involve cross-referencing generated synthetic queries and titles with existing data sources to verify their accuracy and relevance before incorporating them into the retrieval framework. Addressing the computational resource requirements can be achieved by optimizing the data augmentation process, such as batch processing of synthetic queries and titles, caching pre-computed embeddings, and leveraging parallel computing techniques to streamline the augmentation pipeline. Domain-specific fine-tuning is crucial to adapt the LLM-generated data to the specific characteristics and requirements of the retrieval task. By fine-tuning the LLM on domain-specific datasets and incorporating domain knowledge into the augmentation process, the framework can generate more relevant and contextually appropriate synthetic queries and titles.

How can the LLM-augmented retrieval framework be applied to other information retrieval tasks, such as question answering or knowledge base completion, and what additional challenges might arise in those domains?

The LLM-augmented retrieval framework can be applied to other information retrieval tasks, such as question answering or knowledge base completion, by adapting the doc-level embedding process to capture the unique requirements of these tasks. For question answering, the framework can generate synthetic queries that represent potential questions related to the content of the documents. By enriching the embedding of documents with these synthetic queries, the retrieval model can better match relevant documents to user queries in a question answering setting. In knowledge base completion tasks, the framework can leverage LLM-generated titles and passages to enhance the representation of entities or relationships in the knowledge base. This can improve the accuracy of completing missing information in the knowledge base by providing additional context and semantic understanding. Challenges that may arise in these domains include the need for specialized data augmentation techniques tailored to the specific characteristics of question answering and knowledge base completion tasks. Generating synthetic queries or titles that capture the nuances of complex questions or missing knowledge base entries can be challenging and may require domain-specific fine-tuning of the LLM augmentation process. Additionally, ensuring the relevance and accuracy of the augmented data in these tasks is crucial to maintaining the quality of the retrieval results.
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