aiqbench analysis
ScienceMeter introduces a critical evaluation framework for Large Language Models in scientific research, quantifying the trade-offs between retaining existing knowledge, acquiring new information, and projecting future claims. The study reveals significant limitations in current update methods, showing that models struggle to maintain historical accuracy while integrating recent advancements or anticipating emerging trends. This work highlights an essential bottleneck for deploying LLMs as reliable tools in dynamic scientific domains where up-to-date factual precision is paramount.
ScienceMeter: Tracking Scientific Knowledge Updates in Language Models
arXiv:2505.24302v3 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly used to support scientific research, but their knowledge of scientific advancements can quickly become outdated. We introduce ScienceMeter, a new framework for evaluating scientific knowledge update methods over scientific knowledge spanning the past, present, and future. ScienceMeter defines three metrics: knowledge preservation, the extent to which models' understanding of previously learned papers is preserved; knowledge acquisition, how well scientific claims from newly introduced papers are acquired; and knowledge projection, the ability of the updated model to anticipate or generalize to related scientific claims that may emerge in the future. Using ScienceMeter, we evaluate the scientific knowledge of LLMs through claim judgment and generation tasks on a curated dataset across ten domains. We evaluate five representative knowledge update approaches and find that the best-performing knowledge update methods can preserve only 85.9% of existing knowledge, acquire 71.7% of new knowledge, and project 37.7% of future knowledge, underscoring that developing robust scientific knowledge update mechanisms is both crucial and challenging.
Abstract:Large Language Models (LLMs) are increasingly used to support scientific research, but their knowledge of scientific advancements can quickly become outdated. We introduce ScienceMeter, a new framework for evaluating scientific knowledge update methods over scientific knowledge spanning the past, present, and future. ScienceMeter defines three metrics: knowledge preservation, the extent to which models' understanding of previously learned papers is preserved; knowledge acquisition, how well scientific claims from newly introduced papers are acquired; and knowledge projection, the ability of the updated model to anticipate or generalize to related scientific claims that may emerge in the future. Using ScienceMeter, we evaluate the scientific knowledge of LLMs through claim judgment and generation tasks on a curated dataset across ten domains. We evaluate five representative knowledge update approaches and find that the best-performing knowledge update methods can preserve only 85.9% of existing knowledge, acquire 71.7% of new knowledge, and project 37.7% of future knowledge, underscoring that developing robust scientific knowledge update mechanisms is both crucial and challenging.
Computation and Language (cs.CL)
arXiv:2505.24302 [cs.CL]
(or arXiv:2505.24302v3 [cs.CL] for this version)
Full text under the publisher's open licence · 175 words
