Explainable Retrieval

Original Paper: https://arxiv.org/abs/2211.02405

Code Sample: Embed GitHub

Explainable Retrieval in Document Search is a technique that enhances traditional information retrieval systems by not only identifying relevant documents based on user queries but also providing clear explanations for the relevance of each retrieved document. This approach combines vector-based similarity search with natural language explanations, aiming to improve the transparency and interpretability of the retrieval process.

Overview of Explainable Retrieval

Mechanism

1. Vector-Based Similarity Search: This involves representing both documents and queries as vectors in a high-dimensional space. The similarity between a query and documents is computed using metrics like cosine similarity, allowing the system to rank documents based on their relevance to the query.
2. Natural Language Explanations: Alongside the retrieval of documents, the system generates textual explanations that articulate why each document is deemed relevant. This may involve highlighting key terms from the query that match terms in the document, or summarizing the content of the document in relation to the query.

Process

• Document Representation: Documents are indexed and transformed into vector representations, often using techniques such as TF-IDF or embeddings from models like BERT.
• Query Processing: User queries are similarly transformed into vector space.
• Retrieval and Ranking: The system retrieves documents based on their vector similarity to the query and ranks them accordingly.
• Explanation Generation: For each retrieved document, the system generates explanations that clarify the reasoning behind its relevance, which can involve identifying matching keywords or summarizing content.

Advantages

1. Improved Transparency: Users can understand why certain documents were retrieved, which builds trust in the system and helps them make informed decisions.
2. Enhanced User Experience: By providing explanations, users can quickly assess the relevance of documents without needing to read through all retrieved content.
3. Facilitates Learning: Users can learn from the explanations, which can help them refine their queries in future searches.
4. Error Identification: The explanations can help users identify potential misunderstandings in their queries or the retrieval process itself.

Limitations

1. Complexity of Explanation Generation: Generating accurate and meaningful explanations can be challenging, especially for complex documents or queries. The explanations must be concise yet informative.
2. Performance Overhead: The additional processing required to generate explanations may slow down the retrieval process, particularly in large-scale systems.
3. Potential for Misleading Explanations: If not implemented carefully, the explanations might not accurately reflect the true relevance of the documents, leading to confusion.
4. User Dependency: The effectiveness of explanations may vary based on the user's prior knowledge and understanding of the subject matter, potentially limiting the technique's utility for all users.

In conclusion, Explainable Retrieval in Document Search represents a significant advancement in information retrieval systems by combining effective document retrieval with interpretability. While it offers numerous advantages in terms of user trust and experience, challenges remain in ensuring the accuracy and efficiency of the explanations provided.

This is an AI generated summary by Athina AI

Relevant Links:

1. https://www.upgrad.com/blog/information-retrieval-system-explained/ 2. https://www.geeksforgeeks.org/what-is-information-retrieval/ 3. https://arxiv.org/html/2312.10997v5 4. https://en.wikipedia.org/wiki/Information_retrieval 5. https://www.linkedin.com/pulse/retrieval-augmented-generation-guide-effective-search-mike-callahan-edldc 6. https://research.ibm.com/blog/retrieval-augmented-generation-RAG 7. https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf 8. https://www.researchgate.net/publication/367216957_Towards_Improving_the_Explainability_of_Text-based_Information_Retrieval_with_Knowledge_Graphs