Improving Search Relevance with Language Models

Query Expansion is a technique used in search systems to help find more relevant documents. When a user types in a search term, the system can add related words to the original search to increase the chances of retrieving useful information. The goal is to help users see documents that may not have the exact words they typed but are still relevant to their needs.

#Traditional Methods of Query Expansion

In the past, many systems used a method called Pseudo-relevance Feedback (PRF) for query expansion. This method works by looking at the top documents retrieved from the initial search and assuming they are relevant. From these documents, the system extracts new terms to add to the original query. However, if the initial documents are not truly relevant, the new terms generated may not improve search results. This can be a problem when the search term is short or vague, leading to less effective results.

#The Role of Large Language Models

Recently, there has been growing interest in using Large Language Models (LLMs) for query expansion. LLMs are advanced computer models that can generate text and respond to questions because they are trained on vast amounts of information. Their ability to create new terms for queries can be beneficial compared to traditional methods, as they do not rely solely on previously retrieved documents.

#Different Approaches to Query Expansion

In studying how to use LLMs for query expansion, various techniques or prompts have been explored. These can be categorized into three main approaches:

1. Zero-shot prompts: These prompts simply give a basic instruction along with the original query.
2. Few-shot prompts: These include examples of other queries and their corresponding expansions to guide the LLM.
3. Chain of Thought (CoT) prompts: These prompts ask the model to explain its reasoning step-by-step, leading to more detailed and useful expansions.

Research has shown that CoT prompts often yield the best results because they encourage the model to produce a lot of relevant terms.

#Experimental Results and Findings

To test the effectiveness of LLMs for query expansion, experiments were conducted using different datasets, including MS-MARCO and BEIR. These datasets contain various search tasks that allow researchers to see how well different methods perform.

#Results on MS-MARCO

In the MS-MARCO tests, traditional PRF methods provided a good starting point for improving Recall but sometimes hurt the quality of the top search results. When using LLMs for query expansion, different prompts were compared. One key finding was that the CoT prompt helped produce not only higher recall rates but also improved quality in the ranking of the top search results.

The addition of PRF documents enhanced the results even more. This showed that LLMs could effectively use these documents to inform their generated terms, leading to better retrieval outcomes.

#Results on BEIR

The BEIR datasets presented a mixed bag of results. Traditional PRF methods still performed well, especially on data sets that are more specialized, such as academic articles. However, the LLM approaches tended to shine in tasks that required question-answering. It seemed that the LLMs could generate responses that aligned closely with the queries, leading to better retrieval results.

Overall, the results indicated that LLMs can significantly enhance query expansion, especially in certain contexts.

#Understanding Model Size Effects

Another important aspect of the research was the effect of different model sizes on performance. Typically, larger models performed better, which was expected. However, it was noted that the effectiveness of the query expansion method could vary based on the model size used.

Larger models were capable of better performance, but there was also a point at which adding more documents to prompts began to hinder creativity. This suggested that while larger models may have more capabilities, there is a balance to be struck between harnessing the model's potential and providing guidance.

#Limitations of Current Approaches

Despite the promising results, several limitations were noted in the study:

1. The focus was primarily on sparse retrieval methods, which may not capture the full benefits of query expansion in denser retrieval systems.
2. The study specifically looked at certain language models, which although effective, limit findings to those models only.
3. There are many ways to formulate prompts, and the specific templates used may not be the only or best options available.
4. The computational requirements of LLMs could be a challenge for practical deployment in real-world applications.

#Future Directions

Moving forward, there are several avenues for research and improvement. One area could involve looking at the performance of query expansion in dense retrieval settings. Additionally, exploring other types of language models may yield new insights. There is also a need to refine prompt structures to maximize their effectiveness.

Another significant future direction is the practical application of these models in real systems, seeking ways to create smaller models that retain the benefits of larger ones.

#Conclusion

This research highlights the potential of using LLMs for query expansion, demonstrating that they can provide benefits beyond traditional methods. With their ability to understand and generate text, LLMs can create new terms to enhance search results. The findings indicate that using CoT prompts can lead to more meaningful expansions, improving both recall and ranking of results.

As LLMs continue to develop and become more widely available, they may become essential tools for enhancing information retrieval systems. The journey towards better query expansion methods can lead to more effective search engines, ultimately helping users find the information they need more efficiently.