Predicting Information Retrieval Performance Using Relevance Judgments Generated by Large Language Models

Core Concepts

The core message of this paper is to propose a novel query performance prediction (QPP) framework, called QPP-GenRE, which decomposes QPP into independent subtasks of automatically generating relevance judgments using large language models (LLMs). QPP-GenRE can predict various IR evaluation measures based on the generated relevance judgments, and provides interpretable insights into QPP outputs.

Abstract

The paper proposes a novel QPP framework called QPP-GenRE, which decomposes QPP into two main steps: Generating relevance judgments using LLMs: QPP-GenRE employs an LLM, specifically LLaMA, to automatically predict the relevance of each item in the top-n positions of a ranked list for a given query. To improve the LLM's effectiveness in generating relevance judgments, the authors fine-tune LLaMA using parameter-efficient fine-tuning (PEFT) with human-labeled relevance judgments. Predicting IR evaluation measures: QPP-GenRE regards the generated relevance judgments as pseudo-labels to calculate different IR evaluation measures, such as reciprocal rank (RR@10) and normalized discounted cumulative gain (nDCG@10). For predicting recall-oriented measures like nDCG@10, the authors devise an approximation strategy to avoid the high computational cost of judging the entire corpus. The experiments on the TREC 2019-2022 deep learning tracks show that QPP-GenRE achieves state-of-the-art QPP performance in estimating the retrieval quality of both lexical (BM25) and neural (ANCE) rankers, for both precision- and recall-oriented IR evaluation metrics. The authors also analyze the impact of the judgment depth in the ranked list on QPP quality, and demonstrate the effectiveness of fine-tuning LLaMA compared to zero-/few-shot prompting.

Stats

The retrieval quality of BM25 in terms of nDCG@10 is 0.506, 0.480, 0.446 and 0.269 on TREC-DL 19, 20, 21 and 22, respectively. The retrieval quality of ANCE in terms of nDCG@10 is 0.645 and 0.646 on TREC-DL 19 and 20, respectively.

Quotes

None

Key Insights Distilled From

Query Performance Prediction using Relevance Judgments Generated by Large Language Models

by Chuan Meng,N... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.01012.pdf

Deeper Inquiries

How can the generated relevance judgments be further leveraged to improve the performance of the underlying retrieval models, beyond just predicting their performance?

The generated relevance judgments can be further leveraged to improve the performance of the underlying retrieval models in several ways: Relevance Feedback Loop: The relevance judgments can be used to provide feedback to the retrieval models. By analyzing the discrepancies between the predicted relevance and the actual relevance, the retrieval models can be fine-tuned to better understand user intent and improve the accuracy of search results. Query Expansion: Relevance judgments can help in expanding the original query by incorporating terms or concepts that are deemed relevant by the judgments. This can lead to more comprehensive search results and better retrieval performance. Document Ranking: The relevance judgments can be used to re-rank the retrieved documents based on their relevance to the query. By incorporating the judgments into the ranking algorithm, the retrieval models can prioritize more relevant documents, leading to improved search quality. Personalization: Relevance judgments can also be used to personalize search results for individual users. By understanding the relevance judgments assigned by users, the retrieval models can tailor search results to better match the preferences and interests of each user.

What are the potential limitations of using open-source LLMs like LLaMA for generating relevance judgments, and how can these be addressed?

Using open-source LLMs like LLaMA for generating relevance judgments may have the following limitations: Scalability: Open-source LLMs may not be as scalable as commercial LLMs, leading to limitations in handling large volumes of data or complex tasks. This can be addressed by optimizing the model architecture and training process to improve scalability. Resource Constraints: Open-source LLMs may require significant computational resources and time for training and inference. This limitation can be mitigated by optimizing the model for efficiency and utilizing distributed computing resources. Domain Specificity: Open-source LLMs may not be specifically trained for the task of generating relevance judgments in a particular domain. Fine-tuning the LLM on domain-specific data can help address this limitation and improve performance. Bias and Fairness: Open-source LLMs may inherit biases present in the training data, which can impact the quality of relevance judgments. Addressing bias and ensuring fairness in the training data and model architecture is crucial to mitigate this limitation.

How can the QPP-GenRE framework be extended to handle multi-graded relevance judgments, and what would be the implications on predicting different IR evaluation measures?

To extend the QPP-GenRE framework to handle multi-graded relevance judgments, the following steps can be taken: Label Encoding: Modify the framework to accommodate multi-graded relevance labels instead of binary labels. This would involve encoding the relevance judgments into a format that captures the varying degrees of relevance. Model Adaptation: Adjust the LLM-based relevance judgment generation process to predict multi-graded relevance scores. This may require fine-tuning the LLM on multi-graded relevance data to learn the nuances of different relevance levels. Evaluation Metrics: Update the framework to consider evaluation metrics that are suitable for multi-graded relevance judgments, such as Mean Average Precision (MAP) or graded versions of precision and recall. The implications of handling multi-graded relevance judgments in QPP-GenRE include: Enhanced Relevance Understanding: The framework would provide a more nuanced understanding of relevance levels, leading to improved retrieval quality and user satisfaction. Fine-grained Analysis: The ability to predict and analyze multi-graded relevance judgments can offer deeper insights into the performance of retrieval models and help in identifying areas for improvement. Customized Ranking: With multi-graded relevance judgments, the framework can tailor search results based on different relevance levels, providing users with more personalized and relevant information.