DIVERGE: New RAG Framework Improves Diversity in AI-Generated Answers for Open-Ended Questions
Researchers have developed DIVERGE, a new framework for retrieval-augmented generation (RAG) systems that improves the diversity of answers to open-ended questions. Current RAG systems typically assume each query has one correct answer and fail to generate multiple plausible responses even when diverse information is retrieved. The framework achieves approximately 2x improvement in answer diversity without sacrificing quality, addressing a systematic limitation in how AI systems handle questions with multiple valid answers.
A new study published on arXiv presents DIVERGE, a plug-and-play framework designed to enhance diversity in retrieval-augmented generation systems—AI tools that retrieve relevant information to help generate answers. The researchers identified that standard RAG systems struggle with open-ended questions where multiple valid answers exist, such as those requiring creative thinking, fair representation, or inclusive information access. While simply retrieving more diverse sources doesn't guarantee diverse outputs, DIVERGE uses iterative, reflection-guided exploration and diversity-aware retrieval to improve the diversity-quality trade-off. The team also introduced new evaluation metrics specifically designed to measure this trade-off in open-ended question answering. Testing across multiple datasets and language models showed DIVERGE consistently outperformed competing approaches, roughly doubling answer diversity while maintaining answer quality.
What's missing
The paper does not discuss potential limitations of the diversity-focused approach, such as scenarios where excessive diversity might be undesirable (e.g., factual queries requiring a single correct answer), computational overhead of the iterative framework, or how the approach scales to very large retrieval corpora.
What different sources said
- arXiv cs.AICenter
DIVERGE: Diversity-Enhanced RAG for Open-Ended Information Seeking
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