Rethinking Retrieval-Augmented Generation as a Cooperative Decision-Making Problem
arXiv:2602.18734v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success, many existing RAG systems are built based on a ranking-centric, asymmetric dependency paradigm, where the generation quality of the generator is highly dependent on reranking results of the reranker. To overcome this limitation, we reformulate RAG as a cooperative multi-agent decision-making problem and propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework in which the reranker and the generator act as peer decision-makers rather than being connected through an asymmetric dependency pipeline. By jointly optimizing their behaviors toward a shared task objective, the reranker and generator are encouraged to cooperate, ensuring that document reranking and generation work in concert to improve the final response. Experimental results demonstrate g
arXiv:2602.18734v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has demonstrated strong effectiveness in knowledge-intensive tasks by grounding language generation in external evidence. Despite its success, many existing RAG systems are built based on a ranking-centric, asymmetric dependency paradigm, where the generation quality of the generator is highly dependent on reranking results of the reranker. To overcome this limitation, we reformulate RAG as a cooperative multi-agent decision-making problem and propose Cooperative Retrieval-Augmented Generation (CoRAG), a framework in which the reranker and the generator act as peer decision-makers rather than being connected through an asymmetric dependency pipeline. By jointly optimizing their behaviors toward a shared task objective, the reranker and generator are encouraged to cooperate, ensuring that document reranking and generation work in concert to improve the final response. Experimental results demonstrate good generalization and improved generation stability of CoRAG, even when the model is trained on only around 10K PopQA samples. Our model released in https://anonymous.4open.science/r/CoRAG-D63F
Executive Summary
This article proposes a novel framework, Cooperative Retrieval-Augmented Generation (CoRAG), which reformulates Retrieval-Augmented Generation (RAG) as a cooperative multi-agent decision-making problem. CoRAG encourages the reranker and generator to work in concert by jointly optimizing their behaviors toward a shared task objective. Experimental results demonstrate good generalization and improved generation stability of CoRAG, even when trained on limited data. This framework has the potential to overcome the limitations of existing RAG systems and improve the effectiveness of language generation in knowledge-intensive tasks.
Key Points
- ▸ RAG systems are often built based on a ranking-centric, asymmetric dependency paradigm, which can limit their effectiveness.
- ▸ CoRAG reformulates RAG as a cooperative multi-agent decision-making problem, encouraging the reranker and generator to work together.
- ▸ CoRAG achieves good generalization and improved generation stability, even when trained on limited data.
- ▸ CoRAG has the potential to overcome the limitations of existing RAG systems and improve language generation in knowledge-intensive tasks.
Merits
Strength in Cooperative Framework
CoRAG's cooperative framework allows for better collaboration between the reranker and generator, leading to improved generation quality and stability.
Improved Generalization
CoRAG achieves good generalization even when trained on limited data, making it a promising solution for real-world applications.
Demerits
Complexity in Implementation
CoRAG's cooperative framework may be more complex to implement than traditional RAG systems, requiring additional computational resources and expertise.
Limited Evaluation
The article's evaluation of CoRAG is limited to a single task and dataset, and further evaluation is needed to confirm its effectiveness in other scenarios.
Expert Commentary
The article presents a novel and promising approach to improving the effectiveness of RAG systems. CoRAG's cooperative framework has the potential to overcome the limitations of existing RAG systems and improve language generation in knowledge-intensive tasks. However, further evaluation and refinement of the framework are needed to confirm its effectiveness in a wider range of scenarios. Additionally, the complexity of implementing CoRAG may be a challenge for some researchers and practitioners. Overall, the article makes a significant contribution to the field of natural language processing and AI, and its findings have important implications for the development of more effective and cooperative AI systems.
Recommendations
- ✓ Future research should explore the application of CoRAG to other tasks and datasets to confirm its effectiveness and generalizability.
- ✓ The development of tools and resources to simplify the implementation of CoRAG would be beneficial for researchers and practitioners.