INSTUPR: Instruction-based Unsupervised Passage Reranking with Large Language Models

Core Concepts

Leveraging instruction-following LLMs for unsupervised passage reranking.

Abstract

Abstract: Introduces INSTUPR, an unsupervised passage reranking method based on large language models (LLMs). Utilizes instruction-following capabilities of LLMs for passage reranking without fine-tuning. Employs soft score aggregation and pairwise reranking for effectiveness. Introduction: Deep learning methods like DPR have shown superior performance in information retrieval. Passage reranking is crucial to enhance retrieval accuracy by ranking retrieved passages based on relevance to the query. Related Work: Dense passage retriever (DPR) framework encodes documents and queries into dense representations. Previous work explored LLMs for passage reranking through fine-tuning or unsupervised methods. Our Method: INSTUPR leverages instruction-following LLMs for unsupervised passage reranking. Soft relevance score aggregation technique enhances reranking performance. Pairwise reranking scheme outperforms pointwise while being more computationally expensive. Experiments: Conducted on TREC DL19, DL20, and BEIR benchmarks using BM25 as the base retrieval method. Results show INSTUPR outperforms UPR and achieves comparable performance to state-of-the-art methods. Conclusion: Proposes an instruction-based unsupervised passage reranking method leveraging LLMs effectively. Soft score aggregation and pairwise reranking contribute to improved performance.

Stats

"Experimental results demonstrate that IN-STUPR outperforms unsupervised baselines as well as an instruction-tuned reranker." "We instruct the LLMs to predict a relevance score from 1 to 5 using the Likert scale." "Our proposed soft aggregation method significantly contributes to these improvements."

Quotes

Key Insights Distilled From

InstUPR

by Chao-Wei Hua... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16435.pdf

Deeper Inquiries

How can the computational costs of pairwise reranking be addressed in scenarios with a large number of passage candidates?

Pairwise reranking incurs high computational costs, especially when dealing with a large number of passage candidates. To address this issue, several strategies can be implemented: Sampling Techniques: Instead of evaluating all possible pairs, sampling techniques can be used to select a subset of pairs for evaluation. This reduces the overall computation while still providing meaningful pairwise comparisons. Efficient Algorithms: Implementing efficient algorithms specifically designed for pairwise reranking can help optimize the process and reduce computational overhead. Parallel Processing: Utilizing parallel processing capabilities by distributing computations across multiple processors or GPUs can significantly speed up the pairwise reranking process. Feature Selection: Selecting relevant features and reducing dimensionality before performing pairwise comparisons can streamline the computation without compromising performance.

What ethical considerations should be taken into account when deploying LLMs for information retrieval tasks?

When deploying Large Language Models (LLMs) for information retrieval tasks, several ethical considerations must be taken into account to ensure responsible use: Bias Mitigation: LLMs are prone to biases present in their training data, which may lead to biased search results or recommendations. It is crucial to implement bias mitigation techniques such as debiasing algorithms and diverse dataset curation. Transparency and Explainability: Ensuring transparency in how LLMs operate and making their decision-making processes explainable is essential for building trust with users and understanding potential biases or errors. Privacy Protection: Safeguarding user privacy by handling sensitive information appropriately and implementing robust data protection measures is paramount when using LLMs for information retrieval tasks. Fairness and Inclusivity: Striving for fairness in search results by ensuring equal representation across demographics and avoiding discriminatory outcomes based on race, gender, or other protected characteristics.

How do different LLMs exhibit varying behaviors and performances in the context of passage reranking?

Different Large Language Models (LLMs) exhibit varying behaviors and performances in passage reranking due to factors such as model architecture, pretraining objectives, fine-tuning strategies, dataset diversity, among others: Model Architecture - Variations in architecture like transformer-based models vs BERT-based models impact how well they understand context cues during reranking tasks. Pretraining Objectives - Models pretrained on specific objectives like language modeling vs knowledge distillation may excel differently at understanding query-passage relevance signals. Fine-Tuning Strategies - The fine-tuning process on domain-specific data or task-specific instructions influences how well an LLM adapts to passage reranking requirements. 4 .Dataset Diversity - Training datasets used during pretraining/fine-tuning affect an LLM's ability to generalize across various domains/tasks impacting its performance consistency. These differences highlight the importance of selecting appropriate LLMs based on specific requirements while considering trade-offs between model complexity/computation cost versus performance gains in passage reranking tasks."