RPDR: A Round-trip Prediction-Based Data Augmentation Framework for Long-Tail Question Answering
arXiv:2602.17366v1 Announce Type: new Abstract: Long-tail question answering presents significant challenges for large language models (LLMs) due to their limited ability to acquire and accurately recall less common knowledge. Retrieval-augmented generation (RAG) systems have shown great promise in mitigating this limitation by integrating external retrieval mechanisms. However, dense retrieval models often face the same difficulties when generalizing to rare or niche knowledge. In this study, we introduce RPDR, a novel data augmentation framework that selects high-quality easy-to-learn training data, to enhance dense retrievers. Our approach is built around three core components: synthetic data generation, data selection with Round-Trip prediction to identify easy-to-learn instances, and retriever training with these instances. We evaluate RPDR on two long-tail retrieval benchmarks, PopQA and EntityQuestion, demonstrating substantial improvements over existing retrievers like BM25 an
arXiv:2602.17366v1 Announce Type: new Abstract: Long-tail question answering presents significant challenges for large language models (LLMs) due to their limited ability to acquire and accurately recall less common knowledge. Retrieval-augmented generation (RAG) systems have shown great promise in mitigating this limitation by integrating external retrieval mechanisms. However, dense retrieval models often face the same difficulties when generalizing to rare or niche knowledge. In this study, we introduce RPDR, a novel data augmentation framework that selects high-quality easy-to-learn training data, to enhance dense retrievers. Our approach is built around three core components: synthetic data generation, data selection with Round-Trip prediction to identify easy-to-learn instances, and retriever training with these instances. We evaluate RPDR on two long-tail retrieval benchmarks, PopQA and EntityQuestion, demonstrating substantial improvements over existing retrievers like BM25 and Contriver, especially on extremely long-tail categories. We identify the strengths and limitations of RPDR through detailed human analysis and propose a dynamic routing mechanism to dynamically route queries to specialized retrieval modules to further improve retrieval performance.
Executive Summary
The article introduces RPDR, a novel data augmentation framework designed to enhance dense retrievers for long-tail question answering. RPDR selects high-quality easy-to-learn training data through synthetic data generation, data selection with Round-Trip prediction, and retriever training. The framework demonstrates substantial improvements over existing retrievers on long-tail retrieval benchmarks, particularly on extremely long-tail categories. The study also proposes a dynamic routing mechanism to further improve retrieval performance.
Key Points
- ▸ Introduction of RPDR, a data augmentation framework for long-tail question answering
- ▸ Use of Round-Trip prediction to identify easy-to-learn instances
- ▸ Evaluation on PopQA and EntityQuestion benchmarks, showing improvements over existing retrievers
Merits
Improved Retrieval Performance
RPDR demonstrates substantial improvements over existing retrievers, particularly on extremely long-tail categories
Effective Data Selection
The Round-Trip prediction approach effectively identifies easy-to-learn instances, enhancing the quality of the training data
Demerits
Limited Generalizability
The framework's performance may not generalize to all types of long-tail question answering tasks or datasets
Computational Requirements
The use of synthetic data generation and Round-Trip prediction may increase computational requirements
Expert Commentary
The introduction of RPDR represents a significant advancement in the field of long-tail question answering. By leveraging Round-Trip prediction to select high-quality training data, RPDR demonstrates substantial improvements over existing retrievers. However, the framework's limited generalizability and computational requirements must be carefully considered. Further research is needed to explore the applicability of RPDR to diverse question answering tasks and to optimize its performance. The proposed dynamic routing mechanism is a promising direction for future work, as it may enable more efficient and effective retrieval.
Recommendations
- ✓ Future studies should investigate the applicability of RPDR to diverse question answering tasks and datasets
- ✓ Optimization techniques should be explored to reduce the computational requirements of RPDR
