Simplifying Document Question-Answering: Analyzing Semantic Embedding APIs for Realistic Retrieval Scenarios

Simplifying Document Question-Answering: Analyzing Semantic Embedding APIs for Realistic Retrieval Scenarios

Abstract:
The increasing size of language models has limited their accessibility, prompting companies and startups to offer access through APIs. One such API is the semantic embedding API, which generates vector representations of text. In this article, we aim to analyze semantic embedding APIs in realistic retrieval scenarios and provide insights for practitioners and researchers. We specifically focus on domain generalization and multilingual retrieval and evaluate the APIs using BEIR and MIRACL benchmarks. Our findings suggest that re-ranking BM25 results using the APIs is a cost-effective approach, particularly for English retrieval. However, for non-English retrieval, a hybrid model with BM25 yields the best results at a higher cost. This work serves as a foundation for evaluating critical APIs in search and information retrieval.

Introduction:
As language models continue to grow in size, accessing them becomes more challenging. To address this, various companies and startups offer APIs that provide access to large language models. One such API is the semantic embedding API, which generates vector representations of text. In this article, we delve into the analysis of semantic embedding APIs in realistic retrieval scenarios, focusing on domain generalization and multilingual retrieval. By evaluating these APIs using established benchmarks, we aim to assist practitioners and researchers in selecting suitable services based on their specific needs.

Analyzing Semantic Embedding APIs:
To evaluate the capabilities of existing semantic embedding APIs, we conducted experiments using the BEIR and MIRACL benchmarks. These benchmarks offer insights into the performance of the APIs in different retrieval scenarios. Our analysis revealed interesting findings regarding the effectiveness and cost-efficiency of using these APIs.

Improving Retrieval Performance:
In the case of English retrieval, we found that re-ranking BM25 results using the semantic embedding APIs significantly improved the retrieval performance. This approach proved to be cost-effective and yielded accurate and relevant results. By leveraging the semantic embedding API as a re-ranker, developers can enhance the retrieval process and provide more accurate answers to user queries.

Challenges in Non-English Retrieval:
For non-English retrieval, we observed that re-ranking BM25 results still improved the performance, albeit to a lesser extent than in English retrieval. However, a hybrid model combining BM25 and the semantic embedding API as a first-stage retriever yielded the best results. While this approach proved effective, it came at a higher cost. Developers must consider the trade-off between performance and cost when implementing non-English retrieval systems.

The Role of Domain Generalization:
Domain generalization is crucial in retrieval scenarios where the target domain may differ from the training domain. Our analysis revealed that the semantic embedding API, particularly the ADA model, performed well in domain generalization tasks. This capability makes ADA an excellent choice for embedding text in various domains and enhancing retrieval accuracy across different domains.

Enabling Multilingual Retrieval:
One of the key advantages of semantic embedding APIs is their ability to handle multilingual retrieval. These APIs can process input documents in multiple languages and provide answers in the language of the user's query. This eliminates language barriers and facilitates the extraction of relevant information regardless of the language used. Multilingual retrieval opens up opportunities for global applications and promotes inclusive access to information.

Actionable Advice:
Based on our analysis and findings, we provide the following actionable advice for developers and researchers working with semantic embedding APIs for document question-answering:

1. Consider using the semantic embedding API as a re-ranker: Re-ranking BM25 results using the semantic embedding API can significantly improve retrieval performance, particularly in English retrieval scenarios. This approach provides accurate and relevant answers to user queries while maintaining cost-effectiveness.

2. Evaluate the trade-off between performance and cost in non-English retrieval: For non-English retrieval, a hybrid model combining BM25 and the semantic embedding API as a first-stage retriever yields the best results. However, this approach may come at a higher cost. Developers should carefully consider the cost-performance trade-off when designing non-English retrieval systems.

3. Leverage the domain generalization capabilities of the semantic embedding API: If your retrieval scenario involves different domains, consider using the ADA model of the semantic embedding API. ADA performs well in domain generalization tasks, making it suitable for embedding text in various domains and enhancing retrieval accuracy across different domains.

Conclusion:
The analysis of semantic embedding APIs in realistic retrieval scenarios provides valuable insights for practitioners and researchers. By understanding the strengths and limitations of these APIs, developers can optimize document question-answering systems and provide users with accurate and relevant answers. As the field of language models and retrieval systems continues to evolve, the widespread adoption of semantic embedding APIs holds the potential to transform applications and enable a more seamless and futuristic interaction between users and intelligent systems.