Advancing Similarity Search with GenAI: A Retrieval Augmented Generation Approach (2501.04006v1)

Abstract: This article introduces an innovative Retrieval Augmented Generation approach to similarity search. The proposed method uses a generative model to capture nuanced semantic information and retrieve similarity scores based on advanced context understanding. The study focuses on the BIOSSES dataset containing 100 pairs of sentences extracted from the biomedical domain, and introduces similarity search correlation results that outperform those previously attained on this dataset. Through an in-depth analysis of the model sensitivity, the research identifies optimal conditions leading to the highest similarity search accuracy: the results reveals high Pearson correlation scores, reaching specifically 0.905 at a temperature of 0.5 and a sample size of 20 examples provided in the prompt. The findings underscore the potential of generative models for semantic information retrieval and emphasize a promising research direction to similarity search.

• The paper introduces a Retrieval Augmented Generation approach that integrates generative models with prompt engineering for semantic similarity search.
• It achieves a superior Pearson correlation of 0.905 on the BIOSSES dataset by optimizing the temperature (0.5) and using 20 example prompts.
• The approach redefines similarity search by employing conversational dynamics, paving the way for improved document retrieval and recommendation systems.

Insights into Retrieval Augmented Generation for Similarity Search

The paper "Advancing Similarity Search with GenAI: A Retrieval Augmented Generation Approach" by Jean Bertin presents a novel methodology for enhancing similarity search using generative AI. The focus is on improving the retrieval of semantic similarity scores through an innovative approach that integrates generative models with retrieval techniques—known as Retrieval Augmented Generation (RAG). This paper explores how generative models can provide nuanced context understanding and semantic information retrieval, yielding promising results on the BIOSSES dataset.

The research is carried out using the BIOSSES dataset, which comprises 100 pairs of biomedical sentences rated for similarity. This dataset provides an ideal testing ground due to its coverage of a broad semantic similarity spectrum. The proposed RAG approach demonstrates superior Pearson correlation scores compared to previous methods applied to this dataset, achieving a correlation of 0.905 under optimal conditions. These optimal conditions involve a temperature parameter of 0.5 and a prompt size of 20 examples, indicating a delicate interplay between prompt formulation and model configuration.

Method and Technical Rigor

The methodology deviates from conventional vector-based similarity search by employing a conversational chain built upon a generative model. This paradigm shift relies on prompt engineering to transform similarity search into an intelligent discourse. The system prompt comprises structured examples to guide generative synthesis, while user prompts iterate over the test data pairs. The novelty lies in evaluating similarity not just through vector distances but also through intelligent interpretation by the AI model.

In assessing the method's performance, Pearson correlation serves as a key metric. The calculation involves comparing model-derived similarity scores with the BIOSSES dataset's reference scores. This straightforward but robust metric helps ascertain the generative model's efficacy in capturing semantic nuances.

Results

In analyzing sensitivity to parameters, the results hinge on the model's temperature setting and the quantity of example prompts used. A temperature value of 0.5 leads to the highest Pearson coefficient, offering flexibility for the model in interpreting semantic variations. Similarly, including approximately 20 training instances as examples in prompts yields optimal performance, demonstrating the generative model's ability to enhance similarity evaluation with suitable context and examples.

Implications and Future Directions

This paper's findings illuminate the potential for generative models to revolutionize similarity searches by integrating conversational dynamics into the process. Practically, this can advance applications in document retrieval, recommendation systems, and other domains that depend on nuanced semantic comprehension. Theoretically, these results enrich the understanding of retrieval augmented generation, suggesting a paradigm shift from traditional metric-based similarity calculations to context-aware semantic evaluation.

Future research could build upon these findings by optimizing prompt engineering strategies or testing across diverse datasets. Additionally, there's potential to explore the deployment of various generative models and refine this methodology's computational efficiency. Given the rapid advancements in AI, future studies might examine the effects of even more sophisticated model architectures and larger-scale datasets on similarity scoring accuracy.

This research opens new avenues for exploring and enhancing similarity search processes, highlighting an innovative use of generative AI that could redefine semantic similarity assessments across various domains.