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

• URL: http://arxiv.org/abs/2406.05606v1
• Date: Sun, 9 Jun 2024 01:16:04 GMT
• Title: GrowOVER: How Can LLMs Adapt to Growing Real-World Knowledge?
• Authors: Dayoon Ko, Jinyoung Kim, Hahyeon Choi, Gunhee Kim,
• Abstract summary: We propose GrowOVER-QA and GrowOVER-Dialogue, dynamic open-domain QA and dialogue benchmarks that undergo a continuous cycle of updates. Our research indicates that retrieval-augmented language models (RaLMs) struggle with knowledge that has not been trained on or recently updated. We introduce a novel retrieval-interactive language model framework, where the language model evaluates and reflects on its answers for further re-retrieval.
• Score: 36.987716816134984
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the real world, knowledge is constantly evolving, which can render existing knowledge-based datasets outdated.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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