Long Language Models (LLMs) have proven remarkably effective in addressing generic questions. An LLM can be fine-tuned using a company's proprietary documents to meet specific company needs. However, this process is computationally intensive and has several limitations. Fine-tuning can lead to problems such as the curse of inversion, where the model's ability to generalize new knowledge is hampered.
Retrieval Augmented Generation (RAG) offers a more adaptable and scalable method for managing large document collections as an alternative. An LLM, a document database, and an embedding model comprise the three main parts of RAG. It preserves semantic information by embedding document segments into a database during the offline preparation stage.
However, despite its benefits, RAG has a unique set of difficulties, especially when it comes to domain-specific articles. Domain-specific jargon and acronyms, which can only be found in proprietary articles, are a major problem as they can cause the LLM to misunderstand or have hallucinations. Even techniques such as corrective RAG and autonomous RAG suffer when user queries contain unclear technical terms, which can make retrieval of relevant documents unsuccessful.
In a recent research, a team of researchers presented the Golden Retriever framework, a tool created to explore and query large stores of industrial knowledge more effectively. Golden Retriever presents a unique strategy that improves the question-answering procedure prior to document retrieval. Golden Retriever's main innovation is its reflection-based question improvement phase, which is conducted prior to any document retrieval.
The first step in this procedure is to find any jargon or acronyms in the user's input query. Once these terms are found, the framework examines the context in which they are used to clarify their meaning. This is important because general-purpose models can misinterpret specialized language used in technical fields.
Golden Retriever uses an extensive approach. It starts by extracting all acronyms and jargon from the input question and enumerates them. After that, the system consults a pre-compiled list of domain-relevant contexts to determine the context of the question. A jargon dictionary is then consulted to retrieve more detailed definitions and descriptions of the phrases that have been detected. By clearing up any ambiguities and providing clear context, this improved understanding of the question ensures that the RAG framework will select documents that are most relevant to the user’s query when it receives them.
Three open-source LLMs have been used to evaluate Golden Retriever on a domain-specific question-answer dataset, demonstrating its effectiveness. According to these evaluations, Golden Retriever performs better than conventional techniques and offers a reliable option for integrating and querying large stores of industrial knowledge. It greatly improves the accuracy and relevance of retrieved information by ensuring that the context and meaning of domain-specific jargon is understood prior to document retrieval. This makes it a valuable tool for organizations with large, specialized knowledge bases.
The team has summarized its main contributions as follows.
- The team has recognized and addressed the challenges posed by using LLM to query knowledge bases in practical applications, especially with respect to interpreting context and handling domain-specific jargon.
- An improved version of the RAG framework has been presented. Using this method, which includes a reflection-based question expansion stage prior to document retrieval, RAG can more reliably find relevant documents even in situations where terminology may be unclear or context may be inadequate.
- Three independent open-source LLMs have been used to thoroughly evaluate the performance of Golden Retriever. Experiments on a domain-specific question-answering dataset have shown that Golden Retriever is significantly more accurate and effective than baseline algorithms in extracting relevant information from large-scale knowledge libraries.
Take a look at the PaperAll credit for this research goes to the researchers of this project. Also, don't forget to follow us on twitter.com/Marktechpost”>twitter and join our Telegram Channel and LinkedIn GrAbove!. If you like our work, you will love our fact sheet..
Don't forget to join our Subreddit with over 48 billion users
Find upcoming ai webinars here
Tanya Malhotra is a final year student of the University of Petroleum and Energy Studies, Dehradun, pursuing BTech in Computer Engineering with specialization in artificial intelligence and Machine Learning.
She is a data science enthusiast with good analytical and critical thinking skills, along with a keen interest in acquiring new skills, leading groups, and managing work in an organized manner.
<script async src="//platform.twitter.com/widgets.js” charset=”utf-8″>
