A New Approach to Query Performance Prediction

In the field of information retrieval, or how we search for information, one important task is predicting how well a search system will perform for a given query. This is known as Query Performance Prediction (QPP). Traditional approaches in QPP often struggle because they give a single score that does not always represent how well different search metrics work. This can create confusion, especially when the search results do not align well with the predicted score.

To address these issues, we introduce a new framework that breaks down QPP into smaller, independent tasks. Instead of returning just one score, our approach generates a set of Relevance Judgments for each item in a list of search results. From these judgments, we can calculate various Performance Metrics that provide a clearer picture of the search system's effectiveness.

#The Problem with Traditional QPP Approaches

Most current QPP methods focus on providing a single score that suggests how well a search system did for a query. The downside is that this score may not accurately reflect different evaluation measures. For example, two metrics may show different levels of performance, but a single score cannot convey that distinction. Moreover, using one score makes it hard to interpret the results or fix any identified issues. Clearly, there is a need for a more detailed and interpretable system in QPP.

#Key Limitations

1. Lack of Detail: A single score does not capture the complexity of retrieval quality. Different metrics may show different results, but a single number does not make this clear.

2. Interpretability Issues: Relying solely on one score limits our ability to understand and improve search system performance.

#Our Proposed QPP Framework

We present a framework that uses automatically generated relevance judgments. This method allows us to break down QPP into separate tasks, focusing on the relevance of each item in the search results list. By doing this, we can predict various performance metrics based on the relevance judgments, making the system more interpretable.

#Advantages of the Framework

• Multiple Metric Prediction: The new system can predict any search metric with no extra cost when using the generated relevance judgments as pseudo-labels.
• Enhanced Explanation: It goes beyond simply showing whether a query is easy or difficult. It explains why a query is difficult or easy and identifies potential areas for improvement.

#Generating Relevance Judgments

We decided to use a leading open-source language model, LLaMA, to generate these relevance judgments. By utilizing this model, we ensure scientific reproducibility and build a stronger foundation for our system.

#Challenges Faced

1. High Computational Costs: In predicting certain performance metrics, especially those based on recall, it is necessary to assess all relevant items in a large dataset. This involves significant Computational Resources.

2. Effectiveness of Prompting: Directly prompting the model to generate relevance judgments with few examples often yields poor results.

#Addressing the Challenges

To tackle the high costs of processing all items in the dataset, we developed an approximation strategy. This strategy enables us to predict recall-oriented metrics by checking only a few items in the ranked list instead of the entire corpus. Additionally, to improve the effectiveness of LLaMA in generating relevance judgments, we fine-tune it using human-labeled relevance judgments.

#Experiment Results

Using various datasets, our system showed that it achieves top performance compared to traditional QPP methods, effectively estimating retrieval quality for both lexical and neural ranking systems. This demonstrates that our framework not only overcomes existing limitations but also offers significant improvements in accuracy.

#Importance of QPP in Different Applications

Query Performance Prediction is valuable across various domains. It can help in:

• Query Variants Selection: Choose the best versions of queries to improve search results.
• System Configuration Selection: Optimize configurations of information retrieval systems.
• Reducing the Need for Human Judgment: Help limit the time and effort needed to evaluate search results.

#Comparing Approaches

Currently, different QPP methods can be divided into pre-retrieval and post-retrieval methods. Pre-retrieval methods assess the difficulty of a query before performing the search, while post-retrieval methods analyze the results after they have been retrieved. Our focus is on post-retrieval methods, which are particularly useful.

#Unsupervised vs. Supervised Methods

Unsupervised methods generally do not rely on labeled training data and often use statistical measures to predict performance. These can be effective but might not provide the same accuracy as supervised methods. Supervised QPP methods use labeled data to improve the accuracy of predictions but often require extensive resources for training.

#Expanding the Current Research

Our method introduces an innovative perspective by focusing on generating relevance judgments first, followed by performance predictions. This is a shift in approach compared to existing methods, which usually rely on predefined models or algorithms.

#Real-World Applications

Our work can influence various practical applications, such as:

• Conversational Search: Improving the quality of information retrieved in conversational agents.
• Legal Search: Enhancing retrieval in legal databases to ensure that relevant information is easily found.
• General Internet Search: Improving overall search performance on search engines.

#Methodology Breakdown

Our method operates in two major steps:

1. Generating Relevance Judgments: We instruct our model to produce relevance judgments for items in the ranked list based on the query.
2. Calculating Performance Metrics: After generating these judgments, we calculate various performance metrics based on the relevance information.

#How Relevance Judgments Are Generated

The model generates predicted relevance scores for items in the ranked list, which can then be used to assess performance. This process allows us to look at multiple evaluation metrics rather than relying on a single score.

#Experimental Setup and Data

To validate our approach, we conducted experiments using well-known datasets from the TREC-DL deep learning tracks. These datasets contain queries and their associated labeled relevance judgments.

#Key Metrics for Evaluation

We used common metrics like RR@10 (reciprocal rank at 10) and nDCG@10 (normalized Discounted Cumulative Gain at 10). Each metric provides insight into the retrieval quality, and using multiple metrics allows for a more comprehensive evaluation.

#Insights Gained from Experiments

Through our experimentation, we made several observations:

• Our new framework consistently outperformed traditional baselines in predicting retrieval performance.
• The effects of judging depth were notable. For example, the performance stabilizes after a certain number of judgments.
• Fine-tuning the LLaMA model significantly improved the quality of generated relevance judgments.

#Conclusions and Future Directions

The results of our work indicate a strong potential for our QPP framework. By focusing on generating relevance judgments and using them to calculate performance metrics, we have created a more interpretable and effective system for assessing query performance.

#Future Research Opportunities

There are several avenues for future research, including:

• Integration with Other Models: Testing our framework with different language models to see if they can provide even better performance.
• Incorporating More Metrics: Exploring additional performance metrics beyond RR@10 and nDCG@10 to enhance the framework's applicability.
• Improving Efficiency: Looking into ways to speed up the process, especially in scenarios where computational resources are limited.

Overall, this new approach to QPP offers a more refined method for assessing search performance and presents exciting possibilities for advancing the field of information retrieval.