Handling synonyms is a critical challenge in Retrieval-Augmented Generation (RAG) systems, impacting both retrieval accuracy and generated responses. Allganize addresses this by integrating synonym dictionaries and prompt engineering, significantly improving search recall and response quality. Applying synonym handling across retrieval and generation ensures accurate, domain-specific, and user-friendly AI outputs.
In the fields of Information Retrieval (IR) and Natural Language Processing (NLP), handling synonyms has long been a critical challenge. This issue is particularly significant in specialized domains like healthcare, law, technology, and finance, where the same concept is often expressed in various ways. Failing to accurately match these expressions can lead to missing crucial information.
For example, in healthcare, "Electronic Medical Records" and "EMR" refer to the same concept but are expressed differently. If such synonyms are not managed, users may fail to retrieve desired information.
In Retrieval-Augmented Generation (RAG) systems, this synonym issue significantly impacts both retrieval accuracy and the quality of generated answers. RAG operates in two main stages:
If the Retriever cannot handle synonyms, it may fail to find the correct documents, resulting in the Generator producing inaccurate or incomplete answers. This means the quality of answers can depend on how the question is phrased.
Example Question:
What are the potential issues when a patient directly views their electronic medical records?
Relevant Document (Correct Source):
Allowing patients to view their EMRs is a crucial step toward patient-centered care, but proper education and security measures are essential.
In this case, the user uses the term “electronic medical records,” but the document uses “EMR.” A keyword-based search method like BM25 may fail to recognize “EMR” as equivalent to “electronic medical records,” potentially missing the document entirely. As a result, the user cannot find the relevant document, and the system generates an incorrect answer.
Without proper synonym handling in RAG systems, important information may be overlooked.
The implementation of the Retriever can be categorized into three methods: Keyword Search, Semantic Search, and Hybrid Search.
Keyword Search
In keyword-based search, handling synonyms like “electronic medical records” and “EMR” is often problematic. Traditional keyword matching algorithms, such as BM25, only retrieve exact matches. For instance, searching for “electronic medical records” would not return results containing “EMR,” as synonym handling is not supported.
NLP - Text Encoding: Word2Vec - AI PROJECTS
Hybrid Search combines Keyword Search and Semantic Search, leveraging the strengths of both. As a result, it handles synonyms more effectively than keyword-based search but slightly less so than purely semantic search:
Keyword Search < Hybrid Search < Semantic Search in terms of synonym handling.
Even if the Retriever finds the correct document, the Generator may still fail to provide an accurate answer if it does not properly handle synonyms. For instance:
To address this, Prompt Engineering can be used to explicitly guide the Generator in handling synonyms. For example, prompts can include instructions to recognize and harmonize various synonymous terms.
A key solution to the synonym issue in the Retriever is to build and integrate a synonym dictionary into the search system. In search engines like Elasticsearch (ES), queries can be transformed to include synonyms, expanding the scope of the search.
When generating a search request (query), the system can map the user’s input keyword to a list of synonymous terms for retrieval. For example:
This approach ensures that the search encompasses all relevant expressions present in the document index.
Example of Synonym-based Query Construction:
This configuration allows the system to retrieve documents containing any of these terms, significantly improving recall and accuracy.
The effectiveness of this approach depends on the quality of the synonym dictionary. A well-constructed dictionary tailored to specific domains can significantly improve search performance. Synonym dictionaries can be built either manually or automatically, ensuring that search systems capture domain-specific terminology.
Allganize’s Alli, for instance, combines domain-specific synonym dictionaries with user-managed dictionaries. This dual approach allows the system to continuously update, incorporating new terms and expressions as they emerge.
In the Generation stage, accurately reflecting synonyms is equally crucial. Even if the correct document is retrieved during the search, the generation model may fail to produce clear and precise answers if it does not recognize or utilize synonyms effectively.
To address this, synonyms relevant to the user's query can be explicitly added to the prompt. By doing so, the generation model can better interpret and incorporate synonymous terms into its responses.
Example of a Synonym-Enhanced Prompt:
By incorporating synonymous terms into the prompt, the model is guided to understand and reflect relevant expressions, ensuring more accurate and user-friendly answers.
Here is an example of how Alli integrates synonyms into prompts to enhance the accuracy and clarity of generated answers:
User Input:
What are the risks of patients accessing their electronic medical records?
Synonym-Enhanced Prompt Applied in Alli:
"The term 'electronic medical records' (also known as EMR or Electronic Medical Record) refers to patient medical data stored electronically. Based on this, explain the risks of patients accessing their EMRs."
By explicitly incorporating synonyms such as “EMR” and “Electronic Medical Record” into the prompt, Alli ensures that the generation model recognizes equivalent terms. This approach helps the model provide comprehensive and contextually accurate answers, improving user satisfaction.
The experiment was conducted using 21 questions and corresponding documents. For each test:
The experiment consisted of three versions:
Below are the specific configurations and results for each version.
Outcome:
Outcome:
Outcome:
Applying synonym handling in both the Retriever and Generation stages (v3) yielded the best performance. It demonstrated the importance of addressing synonyms throughout the entire RAG pipeline to ensure high-quality search and response generation.
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Allganize effectively addresses synonym challenges in RAG systems by adopting strategies such as synonym dictionary construction and prompt engineering. These efforts have improved search accuracy and the quality of responses to user queries.
By combining domain-specific synonym dictionaries with user customization capabilities, Allganize provides a robust solution to complex search and response challenges in real-world business environments.
For enterprises seeking a reliable RAG system, Allganize is a trustworthy partner offering cutting-edge solutions to enhance search and response effectiveness. To know more contact us today.