Enhancing Topic Relevance Modeling through Mix-structured Summarization and LLM-based Data Augmentation

Основні поняття

Improving topic relevance modeling by using mix-structured summarization as document input and leveraging large language models for data augmentation.

Анотація

The paper proposes two key approaches to enhance topic relevance modeling in social search scenarios:

Mix-structured Summarization:

Extracts a query-focused summary and a general document summary without considering the query.
Concatenates the two summaries as the document input to the relevance model.
This allows the model to better differentiate between strong relevance (where the document is predominantly about the query) and weak relevance (where the document only contains limited information related to the query).

LLM-based Data Augmentation:

Utilizes the language understanding and generation capabilities of large language models (LLMs) to rewrite queries and generate new queries from documents.
The rewritten and generated queries are paired with the corresponding documents to create new training samples across different relevance categories (strong, weak, irrelevant).
This helps address the challenge of obtaining sufficient and diverse training data for topic relevance modeling.
Offline experiments and online A/B tests show that the proposed approaches can significantly improve the performance of topic relevance modeling in social search scenarios.

Статистика

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Цитати

None

Ключові висновки, отримані з

Improving Topic Relevance Model by Mix-structured Summarization and LLM-based Data Augmentation

by Yizhu Liu,Ra... о arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02616.pdf

Глибші Запити

How can the proposed mix-structured summarization approach be extended to other types of content beyond social search, such as scientific papers or news articles

The mix-structured summarization approach proposed in the context can be extended to other types of content beyond social search, such as scientific papers or news articles, by adapting the summarization process to suit the specific characteristics of the content.
For scientific papers, the mix-structured summarization can involve extracting key information from the abstract, introduction, methodology, results, and conclusion sections. The query-focused summary can focus on the research question or objective, while the document summary can capture the main findings and contributions. By combining these summaries, the model can better understand the relevance between the query and the scientific paper.
Similarly, for news articles, the mix-structured summarization can involve extracting the headline, lead paragraph, key details, and conclusion. The query-focused summary can highlight the main topic or event, while the document summary can provide a concise overview of the entire article. This approach can help in determining the relevance of the news article to a specific query.
By customizing the mix-structured summarization process to the unique characteristics of scientific papers or news articles, the model can effectively evaluate the topic relevance in these domains.

What are the potential limitations of using LLMs for data augmentation, and how can they be addressed to ensure the quality and diversity of the generated training samples

While using LLMs for data augmentation offers significant benefits in generating training samples for topic relevance modeling, there are potential limitations that need to be considered:

Quality of Generated Samples: LLMs may sometimes produce low-quality or irrelevant queries during data augmentation, which can impact the overall performance of the relevance model. To address this, it is essential to implement quality control measures such as filtering out poorly generated samples or fine-tuning the LLM to improve the quality of generated queries.

Diversity of Training Samples: LLMs may generate queries that are similar in structure or content, leading to a lack of diversity in the training data. To enhance diversity, techniques such as introducing randomness in the generation process, incorporating different prompts, or leveraging multiple LLMs with varied architectures can be employed.

Bias in Data Augmentation: LLMs may inadvertently introduce biases into the generated training samples, affecting the model's ability to generalize well across different scenarios. Addressing bias requires careful monitoring of the generated data, identifying and mitigating biases, and ensuring a balanced representation of all relevance categories.

By addressing these limitations through rigorous quality control, diversity enhancement, and bias mitigation strategies, the use of LLMs for data augmentation can be optimized to ensure the quality and diversity of the generated training samples.

How can the insights from this work on topic relevance modeling be applied to other information retrieval tasks, such as document ranking or question answering

The insights from this work on topic relevance modeling can be applied to other information retrieval tasks, such as document ranking or question answering, in the following ways:

Document Ranking: The mix-structured summarization approach can be adapted for document ranking tasks by summarizing the key content of documents and aligning them with user queries. This can help in improving the ranking algorithms by enhancing the understanding of the relevance between queries and documents.

Question Answering: For question answering tasks, the concept of mix-structured summarization can be utilized to extract relevant information from passages or documents to answer user queries effectively. By combining query-focused summaries with document summaries, the model can better identify and present accurate answers to user questions.

Semantic Understanding: The use of LLMs for data augmentation can enhance the semantic understanding of queries and documents in various information retrieval tasks. By generating diverse and relevant training samples, the model can learn to capture nuanced relationships between queries and documents, leading to improved performance in tasks like document ranking and question answering.

By leveraging the insights and methodologies from topic relevance modeling, these approaches can be tailored to suit specific information retrieval tasks and enhance the overall effectiveness of the models in retrieving relevant information for users.