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arXiv · 2406.05606

GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?

Dayoon Ko·Jinyoung Kim·Hahyeon Choi·Gunhee Kim
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Abstract

In the real world, knowledge is constantly evolving, which can render existing knowledge-based datasets outdated. This unreliability highlights the critical need for continuous updates to ensure both accuracy and relevance in knowledge-intensive tasks. To address this, we propose GrowOVER-QA and GrowOVER-Dialogue, dynamic open-domain QA and dialogue benchmarks that undergo a continuous cycle of updates, keeping pace with the rapid evolution of knowledge. Our research indicates that retrieval-augmented language models (RaLMs) struggle with knowledge that has not been trained on or recently updated. Consequently, we introduce a novel retrieval-interactive language model framework, where the language model evaluates and reflects on its answers for further re-retrieval. Our exhaustive experiments demonstrate that our training-free framework significantly improves upon existing methods, performing comparably to or even surpassing continuously trained language models.

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Dayoon Ko, Jinyoung Kim, Hahyeon Choi, Gunhee Kim. 2024-06-09. GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?. https://arxiv.org/abs/2406.05606

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