Evaluating large language models: A comprehensive survey

Z Guo, R **, C Liu, Y Huang, D Shi, L Yu, Y Liu… - arxiv preprint arxiv …, 2023 - arxiv.org
Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Large language model alignment: A survey

T Shen, R **, Y Huang, C Liu, W Dong, Z Guo… - arxiv preprint arxiv …, 2023 - arxiv.org
Recent years have witnessed remarkable progress made in large language models (LLMs).
Such advancements, while garnering significant attention, have concurrently elicited various …

Safetybench: Evaluating the safety of large language models with multiple choice questions

Z Zhang, L Lei, L Wu, R Sun, Y Huang, C Long… - arxiv preprint arxiv …, 2023 - arxiv.org
With the rapid development of Large Language Models (LLMs), increasing attention has
been paid to their safety concerns. Consequently, evaluating the safety of LLMs has become …

Trustgpt: A benchmark for trustworthy and responsible large language models

Y Huang, Q Zhang, L Sun - arxiv preprint arxiv:2306.11507, 2023 - arxiv.org
Large Language Models (LLMs) such as ChatGPT, have gained significant attention due to
their impressive natural language processing capabilities. It is crucial to prioritize human …

COLD: A benchmark for Chinese offensive language detection

J Deng, J Zhou, H Sun, C Zheng, F Mi, H Meng… - arxiv preprint arxiv …, 2022 - arxiv.org
Offensive language detection is increasingly crucial for maintaining a civilized social media
platform and deploying pre-trained language models. However, this task in Chinese is still …

Risk taxonomy, mitigation, and assessment benchmarks of large language model systems

T Cui, Y Wang, C Fu, Y **ao, S Li, X Deng, Y Liu… - arxiv preprint arxiv …, 2024 - arxiv.org
Large language models (LLMs) have strong capabilities in solving diverse natural language
processing tasks. However, the safety and security issues of LLM systems have become the …

Culturellm: Incorporating cultural differences into large language models

C Li, M Chen, J Wang, S Sitaram, X **e - arxiv preprint arxiv:2402.10946, 2024 - arxiv.org
Large language models (LLMs) are reported to be partial to certain cultures owing to the
training data dominance from the English corpora. Since multilingual cultural data are often …

Cobra frames: Contextual reasoning about effects and harms of offensive statements

X Zhou, H Zhu, A Yerukola, T Davidson… - arxiv preprint arxiv …, 2023 - arxiv.org
Warning: This paper contains content that may be offensive or upsetting. Understanding the
harms and offensiveness of statements requires reasoning about the social and situational …

Sgp-tod: Building task bots effortlessly via schema-guided llm prompting

X Zhang, B Peng, K Li, J Zhou, H Meng - arxiv preprint arxiv:2305.09067, 2023 - arxiv.org
Building end-to-end task bots and maintaining their integration with new functionalities using
minimal human efforts is a long-standing challenge in dialog research. Recently large …

A brief survey on safety of large language models

Z Gao, X Liu, Y Lan, Z Yang - Journal of computing and information …, 2024 - hrcak.srce.hr
Sažetak Large Language Models (LLMs) have revolutionized Natural Language Processing
(NLP) and have been widely adopted in various applications such as machine translation …