Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language
  Models' Alignment
Ensuring alignment, which refers to making models behave in accordance with
human intentions [1,2], has become a critical task before deploying large
language models (LLMs) in real-world applications. For instance, OpenAI devoted
six months to iteratively aligning GPT-4 before its release [3]. However, a
major challenge faced by practitioners is the lack of clear guidance on
evaluating whether LLM outputs align with social norms, values, and
regulations. This obstacle hinders systematic iteration and deployment of LLMs.
To address this issue, this paper presents a comprehensive survey of key
dimensions that are crucial to consider when assessing LLM trustworthiness. The
survey covers seven major categories of LLM trustworthiness: reliability,
safety, fairness, resistance to misuse, explainability and reasoning, adherence
to social norms, and robustness. Each major category is further divided into
several sub-categories, resulting in a total of 29 sub-categories.
Additionally, a subset of 8 sub-categories is selected for further
investigation, where corresponding measurement studies are designed and
conducted on several widely-used LLMs. The measurement results indicate that,
in general, more aligned models tend to perform better in terms of overall
trustworthiness. However, the effectiveness of alignment varies across the
different trustworthiness categories considered. This highlights the importance
of conducting more fine-grained analyses, testing, and making continuous
improvements on LLM alignment. By shedding light on these key dimensions of LLM
trustworthiness, this paper aims to provide valuable insights and guidance to
practitioners in the field. Understanding and addressing these concerns will be
crucial in achieving reliable and ethically sound deployment of LLMs in various
applications.
64d595fe02e58cc1fdd693d4
Yang Liu
64d595fe02e58cc1fdd693d5
Yuanshun Yao
claimed_verified
2023-08-13T00:11:48.877Z
642f5cd0ee432fcf2e91fbb2
/avatars/f8021d782425bb6bb1c8b70da5f73e55.svg
Kevin Yao
user
kevinyaoresearch
64d595fe02e58cc1fdd693d6
Jean-Francois Ton
64d595fe02e58cc1fdd693d7
Xiaoying Zhang
64d595fe02e58cc1fdd693d8
Ruocheng Guo Hao Cheng
64d595fe02e58cc1fdd693d9
Yegor Klochkov
64d595fe02e58cc1fdd693da
Muhammad Faaiz Taufiq
claimed_verified
2023-08-11T14:37:53.443Z
64d6459436b05ef8ffc7cbd1
/avatars/48c28be2d69ad39e166b82560ee8365a.svg
Muhammad Faaiz Taufiq
user
faaizt
64d595fe02e58cc1fdd693db
Hang Li
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language
  Models' Alignment
Ensuring alignment, which refers to making models behave in accordance with
human intentions [1,2], has become a critical task before deploying large
language models (LLMs) in real-world applications. For instance, OpenAI devoted
six months to iteratively aligning GPT-4 before its release [3]. However, a
major challenge faced by practitioners is the lack of clear guidance on
evaluating whether LLM outputs align with social norms, values, and
regulations. This obstacle hinders systematic iteration and deployment of LLMs.
To address this issue, this paper presents a comprehensive survey of key
dimensions that are crucial to consider when assessing LLM trustworthiness. The
survey covers seven major categories of LLM trustworthiness: reliability,
safety, fairness, resistance to misuse, explainability and reasoning, adherence
to social norms, and robustness. Each major category is further divided into
several sub-categories, resulting in a total of 29 sub-categories.
Additionally, a subset of 8 sub-categories is selected for further
investigation, where corresponding measurement studies are designed and
conducted on several widely-used LLMs. The measurement results indicate that,
in general, more aligned models tend to perform better in terms of overall
trustworthiness. However, the effectiveness of alignment varies across the
different trustworthiness categories considered. This highlights the importance
of conducting more fine-grained analyses, testing, and making continuous
improvements on LLM alignment. By shedding light on these key dimensions of LLM
trustworthiness, this paper aims to provide valuable insights and guidance to
practitioners in the field. Understanding and addressing these concerns will be
crucial in achieving reliable and ethically sound deployment of LLMs in various
applications.
This survey examines key dimensions of trustworthiness for large language models, including reliability, safety, fairness, misuse resistance, explainability, social norms adherence, and robustness, providing guidelines for evaluating and improving model alignment.
large language models LLMs alignment reliability safety fairness misuse resistance explainability social norms robustness trustworthiness