Towards Safer Large Language Models (LLMs)

被引：0

作者：

Lawrence, Carolin ^{[1
]}

Bifulco, Roberto ^{[1
]}

Gashteovski, Kiril ^{[1
]}

Hung, Chia-Chien ^{[1
]}

Ben Rim, Wiem ^{[1
]}

Shaker, Ammar ^{[1
]}

Oyamada, Masafumi ^{[2
]}

Sadamasa, Kunihiko ^{[2
]}

Enomoto, Masafumi ^{[2
]}

Takeoka, Kunihiro ^{[2
]}

机构：

[1] NEC Laboratories Europe, Germany

[2] Data Science Laboratories

来源：

NEC Technical Journal | 2024年 / 17卷 / 02期

关键词：

Computational linguistics - Risk assessment;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

Large Language Models (LLMs) are revolutionizing our world. They have impressive textual capabilities that will fundamentally change how human users can interact with intelligent systems. Nonetheless, they also still have a series of limitations that are important to keep in mind when working with LLMs. We explore how these limitations can be addressed from two different angles. First, we look at options that are currently already available, which include (1) assessing the risk of a use case, (2) prompting a LLM to deliver explanations and (3) encasing LLMs in a human-centred system design. Second, we look at technologies that we are currently developing, which will be able to (1) more accurately assess the quality of an LLM for a high-risk domain, (2) explain the generated LLM output by linking to the input and (3) fact check the generated LLM output against external trustworthy sources. © 2024 NEC Mediaproducts. All rights reserved.

引用

页码：64 / 74

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