CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning (2505.10493v1)

Abstract: Retrieval-Augmented Generation (RAG) is an effective method to enhance the capabilities of LLMs. Existing methods focus on optimizing the retriever or generator in the RAG system by directly utilizing the top-k retrieved documents. However, the documents effectiveness are various significantly across user queries, i.e. some documents provide valuable knowledge while others totally lack critical information. It hinders the retriever and generator's adaptation during training. Inspired by human cognitive learning, curriculum learning trains models using samples progressing from easy to difficult, thus enhancing their generalization ability, and we integrate this effective paradigm to the training of the RAG system. In this paper, we propose a multi-stage Curriculum Learning based RAG system training framework, named CL-RAG. We first construct training data with multiple difficulty levels for the retriever and generator separately through sample evolution. Then, we train the model in stages based on the curriculum learning approach, thereby optimizing the overall performance and generalization of the RAG system more effectively. Our CL-RAG framework demonstrates consistent effectiveness across four open-domain QA datasets, achieving performance gains of 2% to 4% over multiple advanced methods.

Curriculum Learning in Retrieval-Augmented Generation: An Implementation and Evaluation

The paper "CL-RAG: Bridging the Gap in Retrieval-Augmented Generation with Curriculum Learning" proposes a novel framework, CL-RAG, that employs curriculum learning (CL) principles to enhance retrieval-augmented generation (RAG) systems. The authors argue that the integration of curriculum learning, which mimics human cognitive progression from simpler to more complex concepts, offers potential improvements in the performance and generalization of RAG systems, particularly when dealing with variations in document quality retrieved during generation tasks.

Motivation and Methodology

RAG systems, which rely on retrieving additional information from external knowledge bases to enhance the response of LLMs, often suffer due to the inconsistency in the quality of retrieved documents. The discrepancy in the relevance and informativeness of these documents across queries poses challenges for both the retriever and generator components of RAG systems. Traditional approaches utilize the top-k retrieved documents en masse for training, without systematic consideration of their varying difficulty levels.

The CL-RAG framework breaks down the training process into multiple stages using curriculum learning principles. This novel framework first constructs training data with stratified difficulty levels through sample evolution, creating distinct stages. By progressively feeding documents from easy to difficult into retriever and generator training, CL-RAG seeks to ensure improvements in both components' adaptation and resilience to noise.

Experimental Results

The authors tested the CL-RAG framework on four open-domain QA datasets: Natural Questions (NQ), TriviaQA, PopQA, and HotpotQA. Their experimental results indicate a consistent improvement of 2% to 4% in performance metrics (EM and F1 scores) over several baseline methods. Additionally, CL-RAG demonstrated superior robustness when tested under scenarios involving document sets with irrelevant or counterfactual noise, outshining existing RAG configurations.

Contributions and Insights

1. Curriculum Learning Application: CL-RAG represents one of the initial integrations of curriculum learning strategies into RAG systems, demonstrating how structured difficulty progression in training data can enhance model performance and robustness.
2. Document Difficulty Stratification: Through innovative training with classification of document difficulty into Easy, Common, and Hard levels, retrievers and generators gain improved preliminary skills in dealing with a broader range of real-world query complexities.
3. Performance and Robustness: The CL-RAG framework shows superior performance metrics and increased robustness in noisy environments compared to traditional settings, offering a compelling case for the adoption of CL strategies in enhancing RAG systems.

Future Directions and Implications

The potential future applications of CL in AI systems are vast, suggesting that curriculum-based training can be expanded into other tasks where input difficulty levels vary significantly. This research could spark further exploration into training paradigms that seek to augment the generalization ability of AI models in dynamically challenging environments. Additionally, while CL-RAG showcases improvements in response accuracy and stability, further iterations and refinements of the approach could look into how more nuanced measures of difficulty and adaptation beyond retrieval algorithms can be implemented.

The research provides a valuable contribution to both practical applications and theoretical understanding of RAG frameworks, with curriculum learning positioning itself as a viable tactic to addressing ongoing challenges in the field of information retrieval and knowledge augmentation for AI systems.